
import pandas as pd
import os
import snowflake.connector
import seaborn as sns
import matplotlib.pyplot as plt 
import numpy as np
import plotly 
import scipy.stats as stats
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.neighbors import NearestNeighbors


#Enter the password you use to log into snowflake
import getpass 
pwd = getpass.getpass("enter password")

enter password········


#Enter the username of your snowflake account and for account use the url data before .snowflakecomputing.com
#warehouse name can be found by clicking on warehouse on the right hand side of the run button on snowflake
#schema should be public but you can also check for it next to the warehouse button

import snowflake.connector 
conn = snowflake.connector.connect(user='AL4129',
                                   password=str(pwd),
                                   account='szfezvd-columbia',
                                   warehouse='WAREHOUSE',
                                   schema='PUBLIC')


#create a query to pull data from snowflake into your dataset 
#You can use any query you'd typically use to isolate the data you need

q1 = """
WITH BASE AS -- BASE 
(
SELECT *
FROM (
SELECT TI.CM_TRACK, TI.TRACK_NAME, TI.CM_ALBUM,
       TI.ALBUM_NAME, TI.RELEASE_DATE, AI.ARTIST_NAME,
       AI.ARTIST_COUNTRY_CODE2, AI.COUNTRY_NAME, AI.GENRE_ID,
       AI.ARTIST_GENRE, 
       ROW_NUMBER() OVER(PARTITION BY TI.CM_TRACK ORDER BY TI.CM_TRACK DESC) DUPE 
FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_INFO" TI
LEFT JOIN "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_ARTIST_INFO" AI 
ON TI.CM_TRACK = AI.CM_TRACK
LEFT JOIN "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS" TS
ON TI.CM_TRACK = TS.CM_TRACK
WHERE TI.CM_TRACK NOT IN 
   (SELECT DISTINCT CM_TRACK
    FROM (
          SELECT CM_TRACK, 
          COALESCE (SHAZAM_COUNT, 0) AS SHAZAM_COUNT,
          COALESCE (TIKTOK_POSTS, 0) AS TIKTOK_POSTS 
          FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS")
     WHERE SHAZAM_COUNT >= 10000
   )
AND TI.CM_TRACK NOT IN 
   (SELECT DISTINCT CM_TRACK
    FROM (
          SELECT CM_TRACK, 
          COALESCE (SHAZAM_COUNT, 0) AS SHAZAM_COUNT,
          COALESCE (TIKTOK_POSTS, 0) AS TIKTOK_POSTS 
          FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS")
     WHERE TIKTOK_POSTS >= 10000
   )
)
WHERE DUPE = 1
),

T0 AS (
SELECT CM_TRACK,ARTIST_GENRE
FROM (
      SELECT CM_TRACK, ARTIST_GENRE, ROW_NUMBER() OVER (PARTITION BY CM_TRACK ORDER BY ARTIST_GENRE) AS rnk
      FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_ARTIST_INFO") AS t
      WHERE rnk = 1),


T AS (
SELECT c.CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES, SPOTIFY_PLAYS, 
        SPOTIFY_POPULARITY, TIKTOK_POSTS, AIRPLAY_STREAMS, T0.ARTIST_GENRE, 
        CREATED_AT, EXTRACT(MONTH FROM CREATED_AT) AS Month, EXTRACT(YEAR FROM CREATED_AT) AS Year
FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS" AS c LEFT JOIN T0
USING (CM_TRACK)),

T1 AS (
SELECT CM_TRACK, CREATED_AT, Month,
        CASE WHEN Month IN (1,2,3) THEN 'Q1'
        WHEN Month IN (4,5,6) THEN 'Q2'
        WHEN Month IN (7,8,9) THEN 'Q3'
        ELSE 'Q4' END AS Quarters
FROM T
WHERE Year = 2021),

T2 AS (
SELECT CM_TRACK, MAX(CREATED_AT) AS Last_day, Quarters
FROM T1
GROUP BY CM_TRACK, Quarters
ORDER BY CM_TRACK, QUarters),

T3 AS (
SELECT T.CM_TRACK, T2.Quarters, T2.Last_day, 
        YOUTUBE_VIEWS, LAG(YOUTUBE_VIEWS, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_YOUTUBE_VIEWS,
        YOUTUBE_LIKES, LAG(YOUTUBE_LIKES, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_YOUTUBE_LIKES,
        SPOTIFY_PLAYS, LAG(SPOTIFY_PLAYS, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_SPOTIFY_PLAYS,
        SPOTIFY_POPULARITY, LAG(SPOTIFY_POPULARITY, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_SPOTIFY_POPULARITY,
        TIKTOK_POSTS, LAG(TIKTOK_POSTS, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_TIKTOK_POSTS,
        AIRPLAY_STREAMS, LAG(AIRPLAY_STREAMS, 1) OVER(PARTITION BY T.CM_TRACK ORDER BY Quarters) AS Last_AIRPLAY_STREAMS,
        ARTIST_GENRE
FROM T JOIN T2 
ON T.CM_TRACK = T2.CM_TRACK AND T.CREATED_AT = T2.Last_day
ORDER BY T.CM_TRACK, T2.Quarters),

Q1_growth AS (
SELECT T3.CM_TRACK, IFNULL(T3.YOUTUBE_VIEWS - c.MIN_YT_L,0) AS Q1_YOUTUBE_VIEWS_GROWTH, 
        IFNULL(T3.YOUTUBE_LIKES - c.MIN_YT_L,0) AS Q1_YOUTUBE_LIKES_GROWTH,
        IFNULL(T3.SPOTIFY_PLAYS - c.MIN_SPOT_P,0) AS Q1_SPOTIFY_PLAYS_GROWTH,
        IFNULL(T3.SPOTIFY_POPULARITY - c.MIN_SPOT_PP,0) AS Q1_SPOTIFY_POPULARITY_GROWTH,
        IFNULL(T3.TIKTOK_POSTS - c.MIN_TT,0) AS Q1_TIKTOK_POSTS_GROWTH,
        IFNULL(T3.AIRPLAY_STREAMS - c.MIN_AP,0) AS Q1_AIRPLAY_STREAMS_GROWTH, ARTIST_GENRE
FROM T3 LEFT JOIN (SELECT CM_TRACK, MIN(YOUTUBE_VIEWS) AS MIN_YT_V, 
                   MIN(YOUTUBE_LIKES) AS MIN_YT_L, 
                   MIN(SPOTIFY_PLAYS) AS MIN_SPOT_P, 
                   MIN(SPOTIFY_POPULARITY) AS MIN_SPOT_PP, 
                   MIN(TIKTOK_POSTS) AS MIN_TT, 
                   MIN(AIRPLAY_STREAMS) AS MIN_AP 
                   FROM (
                         SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES,
                         SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, 
                         AIRPLAY_STREAMS, CREATED_AT, Month, 
                         CASE WHEN Month IN (1,2,3) THEN 'Q1'
                         WHEN Month IN (4,5,6) THEN 'Q2'
                         WHEN Month IN (7,8,9) THEN 'Q3'
                         ELSE 'Q4' END AS Quarters
                         FROM (
                               SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES,
                                      SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, 
                                      AIRPLAY_STREAMS, CREATED_AT, 
                                      EXTRACT(MONTH FROM CREATED_AT) AS Month, EXTRACT(YEAR FROM CREATED_AT) AS Year
                               FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS"
                               WHERE CREATED_AT BETWEEN '2021-01-01' AND '2021-12-31'
                               )
                        )
                  WHERE Quarters = 'Q1'
                  GROUP BY CM_TRACK
                  ) AS c
USING (CM_TRACK)
WHERE Quarters = 'Q1'),

Q2_Growth AS(
SELECT T3.CM_TRACK, IFNULL(T3.YOUTUBE_VIEWS - c.YOUTUBE_VIEWS,0) AS Q2_YOUTUBE_VIEWS_GROWTH, 
        IFNULL(T3.YOUTUBE_LIKES - c.YOUTUBE_LIKES,0) AS Q2_YOUTUBE_LIKES_GROWTH,
        IFNULL(T3.SPOTIFY_PLAYS - c.SPOTIFY_PLAYS,0) AS Q2_SPOTIFY_PLAYS_GROWTH,
        IFNULL(T3.SPOTIFY_POPULARITY - c.SPOTIFY_POPULARITY,0) AS Q2_SPOTIFY_POPULARITY_GROWTH,
        IFNULL(T3.TIKTOK_POSTS - c.TIKTOK_POSTS,0) AS Q2_TIKTOK_POSTS_GROWTH,
        IFNULL(T3.AIRPLAY_STREAMS - c.AIRPLAY_STREAMS,0) AS Q2_AIRPLAY_STREAMS_GROWTH, ARTIST_GENRE
FROM T3 LEFT JOIN (SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES, SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, AIRPLAY_STREAMS 
                   FROM T3
                   WHERE Quarters = 'Q1') AS c
USING (CM_TRACK)
WHERE Quarters = 'Q2'),

Q3_Growth AS (
SELECT T3.CM_TRACK, IFNULL(T3.YOUTUBE_VIEWS - c.YOUTUBE_VIEWS,0) AS Q3_YOUTUBE_VIEWS_GROWTH, 
        IFNULL(T3.YOUTUBE_LIKES - c.YOUTUBE_LIKES,0) AS Q3_YOUTUBE_LIKES_GROWTH,
        IFNULL(T3.SPOTIFY_PLAYS - c.SPOTIFY_PLAYS,0) AS Q3_SPOTIFY_PLAYS_GROWTH,
        IFNULL(T3.SPOTIFY_POPULARITY - c.SPOTIFY_POPULARITY,0) AS Q3_SPOTIFY_POPULARITY_GROWTH,
        IFNULL(T3.TIKTOK_POSTS - c.TIKTOK_POSTS,0) AS Q3_TIKTOK_POSTS_GROWTH,
        IFNULL(T3.AIRPLAY_STREAMS - c.AIRPLAY_STREAMS,0) AS Q3_AIRPLAY_STREAMS_GROWTH, ARTIST_GENRE
FROM T3 LEFT JOIN (SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES, SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, AIRPLAY_STREAMS 
                   FROM T3
                   WHERE Quarters = 'Q2') AS c
USING (CM_TRACK)
WHERE Quarters = 'Q3'),

Q4_Growth AS (
SELECT T3.CM_TRACK, IFNULL(T3.YOUTUBE_VIEWS - c.YOUTUBE_VIEWS,0) AS Q4_YOUTUBE_VIEWS_GROWTH, 
        IFNULL(T3.YOUTUBE_LIKES - c.YOUTUBE_LIKES,0) AS Q4_YOUTUBE_LIKES_GROWTH,
        IFNULL(T3.SPOTIFY_PLAYS - c.SPOTIFY_PLAYS,0) AS Q4_SPOTIFY_PLAYS_GROWTH,
        IFNULL(T3.SPOTIFY_POPULARITY - c.SPOTIFY_POPULARITY,0) AS Q4_SPOTIFY_POPULARITY_GROWTH,
        IFNULL(T3.TIKTOK_POSTS - c.TIKTOK_POSTS,0) AS Q4_TIKTOK_POSTS_GROWTH,
        IFNULL(T3.AIRPLAY_STREAMS - c.AIRPLAY_STREAMS,0) AS Q4_AIRPLAY_STREAMS_GROWTH, ARTIST_GENRE
FROM T3 LEFT JOIN (SELECT CM_TRACK, YOUTUBE_VIEWS, YOUTUBE_LIKES, SPOTIFY_PLAYS, SPOTIFY_POPULARITY, TIKTOK_POSTS, AIRPLAY_STREAMS 
                   FROM T3
                   WHERE Quarters = 'Q3') AS c
USING (CM_TRACK)
WHERE Quarters = 'Q4'),


QUARTERS_FINAL AS (
SELECT Q4_Growth.CM_TRACK, Q4_Growth.ARTIST_GENRE, 
        IFNULL(Q1_YOUTUBE_VIEWS_GROWTH, 0) AS Q1_YOUTUBE_VIEWS_GROWTH, IFNULL(Q1_YOUTUBE_LIKES_GROWTH, 0) AS Q1_YOUTUBE_LIKES_GROWTH, 
        IFNULL(Q1_SPOTIFY_PLAYS_GROWTH, 0) AS Q1_SPOTIFY_PLAYS_GROWTH, IFNULL(Q1_SPOTIFY_POPULARITY_GROWTH, 0) AS Q1_SPOTIFY_POPULARITY_GROWTH, 
        IFNULL(Q1_TIKTOK_POSTS_GROWTH, 0) AS Q1_TIKTOK_POSTS_GROWTH, IFNULL(Q1_AIRPLAY_STREAMS_GROWTH, 0) AS Q1_AIRPLAY_STREAMS_GROWTH,
        IFNULL(Q2_YOUTUBE_VIEWS_GROWTH, 0) AS Q2_YOUTUBE_VIEWS_GROWTH, IFNULL(Q2_YOUTUBE_LIKES_GROWTH, 0) AS Q2_YOUTUBE_LIKES_GROWTH, 
        IFNULL(Q2_SPOTIFY_PLAYS_GROWTH, 0) AS Q2_SPOTIFY_PLAYS_GROWTH, IFNULL(Q2_SPOTIFY_POPULARITY_GROWTH, 0) AS Q2_SPOTIFY_POPULARITY_GROWTH, 
        IFNULL(Q2_TIKTOK_POSTS_GROWTH, 0) AS Q2_TIKTOK_POSTS_GROWTH, IFNULL(Q2_AIRPLAY_STREAMS_GROWTH, 0) AS Q2_AIRPLAY_STREAMS_GROWTH,
        IFNULL(Q3_YOUTUBE_VIEWS_GROWTH, 0) AS Q3_YOUTUBE_VIEWS_GROWTH, IFNULL(Q3_YOUTUBE_LIKES_GROWTH, 0) AS Q3_YOUTUBE_LIKES_GROWTH, 
        IFNULL(Q3_SPOTIFY_PLAYS_GROWTH, 0) AS Q3_SPOTIFY_PLAYS_GROWTH, IFNULL(Q3_SPOTIFY_POPULARITY_GROWTH, 0) AS Q3_SPOTIFY_POPULARITY_GROWTH, 
        IFNULL(Q3_TIKTOK_POSTS_GROWTH, 0) AS Q3_TIKTOK_POSTS_GROWTH, IFNULL(Q3_AIRPLAY_STREAMS_GROWTH, 0) AS Q3_AIRPLAY_STREAMS_GROWTH,
        IFNULL(Q4_YOUTUBE_VIEWS_GROWTH, 0) AS Q4_YOUTUBE_VIEWS_GROWTH, IFNULL(Q4_YOUTUBE_LIKES_GROWTH, 0) AS Q4_YOUTUBE_LIKES_GROWTH, 
        IFNULL(Q4_SPOTIFY_PLAYS_GROWTH, 0) AS Q4_SPOTIFY_PLAYS_GROWTH, IFNULL(Q4_SPOTIFY_POPULARITY_GROWTH, 0) AS Q4_SPOTIFY_POPULARITY_GROWTH, 
        IFNULL(Q4_TIKTOK_POSTS_GROWTH, 0) AS Q4_TIKTOK_POSTS_GROWTH, IFNULL(Q4_AIRPLAY_STREAMS_GROWTH, 0) AS Q4_AIRPLAY_STREAMS_GROWTH
FROM Q1_Growth FULL JOIN Q2_Growth
ON Q1_Growth.CM_TRACK = Q2_Growth.CM_TRACK AND Q1_Growth.ARTIST_GENRE = Q2_Growth.ARTIST_GENRE
FULL JOIN Q3_Growth
ON Q2_Growth.CM_TRACK = Q3_Growth.CM_TRACK AND Q2_Growth.ARTIST_GENRE = Q3_Growth.ARTIST_GENRE
FULL JOIN Q4_Growth 
ON Q3_Growth.CM_TRACK = Q4_Growth.CM_TRACK AND Q3_Growth.ARTIST_GENRE = Q4_Growth.ARTIST_GENRE
ORDER BY CM_TRACK, ARTIST_GENRE), 


-- Date table: Find the last date of each month for each CM_Track
Date AS(SELECT CM_TRACK,MONTH(Created_at) as Month,MAX(Created_at) as last_day
                   FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS"
                   WHERE YEAR(Created_at) ='2021'
                   GROUP BY Month,CM_TRACK
                   ORDER BY 1,2),
-- MonthlyCTE: Join the platforms performance to date data
     MonthlyCTE AS(SELECT a.CM_TRACK,a.Month,a.last_day,b.YOUTUBE_VIEWS,b.YOUTUBE_LIKES,b.SPOTIFY_PLAYS,b.SPOTIFY_POPULARITY,
                          b.TIKTOK_POSTS,b.AIRPLAY_STREAMS
                   FROM Date a
                   LEFT JOIN "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS" b
                   ON a.CM_TRACK = b.CM_TRACK
                   AND a.last_day = b.created_at
                   WHERE YEAR(Created_at) ='2021'),
-- Genre：Pick up the representive genre of each track based on artist genre rank
         Genre  AS(SELECT CM_TRACK,artist_genre as genre
                   FROM (SELECT c.CM_TRACK,c.artist_genre,
                                row_number()OVER(PARTITION BY CM_TRACK ORDER BY artist_genre) as rnk
                        FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_ARTIST_INFO" c) tmp
                   WHERE rnk=1),
-- Genre_MonthlyCTE:Join the representive genre to monthly platforms performance
Genre_MonthlyCTE AS (SELECT g.genre,d.*
                    FROM MonthlyCTE d
                    LEFT JOIN Genre g
                    USING(CM_TRACK)),
--  Pre_MonthlyCTE: Query last monthly data and join them to each month (EG: May/June,June/July...)
 Pre_MonthlyCTE AS(SELECT CM_TRACK,genre,Month,last_day,YOUTUBE_VIEWS,YOUTUBE_LIKES,SPOTIFY_PLAYS,SPOTIFY_POPULARITY,
                          TIKTOK_POSTS,AIRPLAY_STREAMS,
                          LAG(YOUTUBE_VIEWS,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_YTViews,
                          LAG(YOUTUBE_LIKES,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_YTLikes,
                          LAG(SPOTIFY_PLAYS,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_SpotifyPlays,
                          LAG(SPOTIFY_POPULARITY,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_SpotifyPop,
                          LAG(TIKTOK_POSTS,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_TIKTOKPOSTS,
                          LAG(AIRPLAY_STREAMS,1) OVER(PARTITION BY CM_TRACK ORDER BY Month) as prev_month_AIRPLAY
                   FROM Genre_MonthlyCTE),                   
-- Growth Rate Table： includes perfomance of platforms and the every montly growth                                           
Growth_Rate AS(SELECT CM_TRACK,genre,month,last_day,YOUTUBE_VIEWS,prev_month_YTViews,
                      YOUTUBE_VIEWS-prev_month_YTViews as YTViews_Growth,
                      YOUTUBE_LIKES,prev_month_YTLikes,
                      YOUTUBE_LIKES-prev_month_YTLikes as YTLikes_Growth,
                      SPOTIFY_PLAYS,prev_month_SpotifyPlays,
                      SPOTIFY_PLAYS-prev_month_SpotifyPlays as SpotifyPlays_Growth,
                      SPOTIFY_POPULARITY,prev_month_SpotifyPop,
                      SPOTIFY_POPULARITY-prev_month_SpotifyPop as SpotifyPop_Growth,
                      TIKTOK_POSTS,prev_month_TIKTOKPOSTS,
                      TIKTOK_POSTS-prev_month_TIKTOKPOSTS as TIKTOK_Growth,
                      AIRPLAY_STREAMS,prev_month_AIRPLAY,
                      AIRPLAY_STREAMS-prev_month_AIRPLAY as AIRPLAY_Growth
                FROM Pre_MonthlyCTE),
     YTViews_Growth AS(SELECT CM_TRACK,genre,month,YTViews_Growth
                      FROM Growth_Rate),
                      
MOM_FINAL AS (
SELECT CM_Track,genre,
       MAX(CASE WHEN month='1' THEN YTViews_Growth ELSE 0 END) AS "M1_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='2' THEN YTViews_Growth ELSE 0 END) AS "M2_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='3' THEN YTViews_Growth ELSE 0 END) AS "M3_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='4' THEN YTViews_Growth ELSE 0 END) AS "M4_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='5' THEN YTViews_Growth ELSE 0 END) AS "M5_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='6' THEN YTViews_Growth ELSE 0 END) AS "M6_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='7' THEN YTViews_Growth ELSE 0 END) AS "M7_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='8' THEN YTViews_Growth ELSE 0 END) AS "M8_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='9' THEN YTViews_Growth ELSE 0 END) AS "M9_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='10' THEN YTViews_Growth ELSE 0 END) AS "M10_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='11' THEN YTViews_Growth ELSE 0 END) AS "M11_YOUTUBE_VIEWS_GROWTH",
       MAX(CASE WHEN month='12' THEN YTViews_Growth ELSE 0 END) AS "M12_YOUTUBE_VIEWS_GROWTH",      
       MAX(CASE WHEN month='1' THEN YTLikes_Growth ELSE 0 END) AS "M1_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='2' THEN YTLikes_Growth ELSE 0 END) AS "M2_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='3' THEN YTLikes_Growth ELSE 0 END) AS "M3_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='4' THEN YTLikes_Growth ELSE 0 END) AS "M4_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='5' THEN YTLikes_Growth ELSE 0 END) AS "M5_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='6' THEN YTLikes_Growth ELSE 0 END) AS "M6_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='7' THEN YTLikes_Growth ELSE 0 END) AS "M7_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='8' THEN YTLikes_Growth ELSE 0 END) AS "M8_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='9' THEN YTLikes_Growth ELSE 0 END) AS "M9_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='10' THEN YTLikes_Growth ELSE 0 END) AS "M10_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='11' THEN YTLikes_Growth ELSE 0 END) AS "M11_YOUTUBE_LIKES_GROWTH",
       MAX(CASE WHEN month='12' THEN YTLikes_Growth ELSE 0 END) AS "M12_YOUTUBE_LIKES_GROWTH", 
       MAX(CASE WHEN month='1' THEN SpotifyPlays_Growth ELSE 0 END) AS "M1_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='2' THEN SpotifyPlays_Growth ELSE 0 END) AS "M2_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='3' THEN SpotifyPlays_Growth ELSE 0 END) AS "M3_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='4' THEN SpotifyPlays_Growth ELSE 0 END) AS "M4_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='5' THEN SpotifyPlays_Growth ELSE 0 END) AS "M5_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='6' THEN SpotifyPlays_Growth ELSE 0 END) AS "M6_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='7' THEN SpotifyPlays_Growth ELSE 0 END) AS "M7_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='8' THEN SpotifyPlays_Growth ELSE 0 END) AS "M8_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='9' THEN SpotifyPlays_Growth ELSE 0 END) AS "M9_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='10' THEN SpotifyPlays_Growth ELSE 0 END) AS "M10_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='11' THEN SpotifyPlays_Growth ELSE 0 END) AS "M11_SPOTIFY_PLAYS_GROWTH",
       MAX(CASE WHEN month='12' THEN SpotifyPlays_Growth ELSE 0 END) AS "M12_SPOTIFY_PLAYS_GROWTH",    
       MAX(CASE WHEN month='1' THEN SpotifyPop_Growth ELSE 0 END) AS "M1_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='2' THEN SpotifyPop_Growth ELSE 0 END) AS "M2_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='3' THEN SpotifyPop_Growth ELSE 0 END) AS "M3_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='4' THEN SpotifyPop_Growth ELSE 0 END) AS "M4_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='5' THEN SpotifyPop_Growth ELSE 0 END) AS "M5_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='6' THEN SpotifyPop_Growth ELSE 0 END) AS "M6_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='7' THEN SpotifyPop_Growth ELSE 0 END) AS "M7_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='8' THEN SpotifyPop_Growth ELSE 0 END) AS "M8_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='9' THEN SpotifyPop_Growth ELSE 0 END) AS "M9_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='10' THEN SpotifyPop_Growth ELSE 0 END) AS "M10_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='11' THEN SpotifyPop_Growth ELSE 0 END) AS "M11_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='12' THEN SpotifyPop_Growth ELSE 0 END) AS "M12_SPOTIFY_POPULARITY_GROWTH",
       MAX(CASE WHEN month='1' THEN TIKTOK_Growth ELSE 0 END) AS "M1_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='2' THEN TIKTOK_Growth ELSE 0 END) AS "M2_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='3' THEN TIKTOK_Growth ELSE 0 END) AS "M3_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='4' THEN TIKTOK_Growth ELSE 0 END) AS "M4_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='5' THEN TIKTOK_Growth ELSE 0 END) AS "M5_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='6' THEN TIKTOK_Growth ELSE 0 END) AS "M6_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='7' THEN TIKTOK_Growth ELSE 0 END) AS "M7_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='8' THEN TIKTOK_Growth ELSE 0 END) AS "M8_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='9' THEN TIKTOK_Growth ELSE 0 END) AS "M9_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='10' THEN TIKTOK_Growth ELSE 0 END) AS "M10_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='11' THEN TIKTOK_Growth ELSE 0 END) AS "M11_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='12' THEN TIKTOK_Growth ELSE 0 END) AS "M12_TIKTOK_POSTS_GROWTH",
       MAX(CASE WHEN month='1' THEN AIRPLAY_Growth ELSE 0 END) AS "M1_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='2' THEN AIRPLAY_Growth ELSE 0 END) AS "M2_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='3' THEN AIRPLAY_Growth ELSE 0 END) AS "M3_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='4' THEN AIRPLAY_Growth ELSE 0 END) AS "M4_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='5' THEN AIRPLAY_Growth ELSE 0 END) AS "M5_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='6' THEN AIRPLAY_Growth ELSE 0 END) AS "M6_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='7' THEN AIRPLAY_Growth ELSE 0 END) AS "M7_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='8' THEN AIRPLAY_Growth ELSE 0 END) AS "M8_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='9' THEN AIRPLAY_Growth ELSE 0 END) AS "M9_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='10' THEN AIRPLAY_Growth ELSE 0 END) AS "M10_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='11' THEN AIRPLAY_Growth ELSE 0 END) AS "M11_AIRPLAY_STREAMS_GROWTH",
       MAX(CASE WHEN month='12' THEN AIRPLAY_Growth ELSE 0 END) AS "M12_AIRPLAY_STREAMS_GROWTH"      
FROM Growth_Rate
GROUP BY 1,2),

WOW_FINAL AS (
SELECT CM_TRACK, SUM(A) AS WK1_YT_VIEWS_GROWTH, SUM(B) AS WK2_YT_VIEWS_GROWTH, SUM(C) AS WK3_YT_VIEWS_GROWTH,
       SUM(D) AS WK4_YT_VIEWS_GROWTH, SUM(E) AS WK5_YT_VIEWS_GROWTH, SUM(F) AS WK6_YT_VIEWS_GROWTH,
       SUM(G) AS WK7_YT_VIEWS_GROWTH, SUM(H) AS WK8_YT_VIEWS_GROWTH, SUM(I) AS WK9_YT_VIEWS_GROWTH,
       SUM(J) AS WK10_YT_VIEWS_GROWTH, SUM(K) AS WK11_YT_VIEWS_GROWTH, SUM(L) AS WK12_YT_VIEWS_GROWTH,
       SUM(M) AS WK13_YT_VIEWS_GROWTH, SUM(N) AS WK14_YT_VIEWS_GROWTH, SUM(O) AS WK15_YT_VIEWS_GROWTH, -- END OF YT 
       SUM(P) AS WK1_SPOTIFY_PLAYS_GROWTH, SUM(Q) AS WK2_SPOTIFY_PLAYS_GROWTH, SUM(R) AS WK3_SPOTIFY_PLAYS_GROWTH,
       SUM(S) AS WK4_SPOTIFY_PLAYS_GROWTH, SUM(T) AS WK5_SPOTIFY_PLAYS_GROWTH, SUM(U) AS WK6_SPOTIFY_PLAYS_GROWTH,
       SUM(V) AS WK7_SPOTIFY_PLAYS_GROWTH, SUM(W) AS WK8_SPOTIFY_PLAYS_GROWTH, SUM(X) AS WK9_SPOTIFY_PLAYS_GROWTH,
       SUM(Y) AS WK10_SPOTIFY_PLAYS_GROWTH, SUM(Z) AS WK11_SPOTIFY_PLAYS_GROWTH, SUM(AA) AS WK12_SPOTIFY_PLAYS_GROWTH,
       SUM(BB) AS WK13_SPOTIFY_PLAYS_GROWTH, SUM(CC) AS WK14_SPOTIFY_PLAYS_GROWTH, SUM(DD) AS WK15_SPOTIFY_PLAYS_GROWTH, -- END OF SPOTIFY
       SUM(EE) AS WK1_TIKTOK_POSTS_GROWTH, SUM(FF) AS WK2_TIKTOK_POSTS_GROWTH, SUM(GG) AS WK3_TIKTOK_POSTS_GROWTH,
       SUM(HH) AS WK4_TIKTOK_POSTS_GROWTH, SUM(II) AS WK5_TIKTOK_POSTS_GROWTH, SUM(JJ) AS WK6_TIKTOK_POSTS_GROWTH,
       SUM(KK) AS WK7_TIKTOK_POSTS_GROWTH, SUM(LL) AS WK8_TIKTOK_POSTS_GROWTH, SUM(MM) AS WK9_TIKTOK_POSTS_GROWTH,
       SUM(NN) AS WK10_TIKTOK_POSTS_GROWTH, SUM(OO) AS WK11_TIKTOK_POSTS_GROWTH, SUM(PP) AS WK12_TIKTOK_POSTS_GROWTH,
       SUM(QQ) AS WK13_TIKTOK_POSTS_GROWTH, SUM(RR) AS WK14_TIKTOK_POSTS_GROWTH, SUM(SS) AS WK15_TIKTOK_POSTS_GROWTH
FROM(
     SELECT CM_TRACK, CREATED_AT, 
            SUM(CASE WHEN YEAR_WEEK = 1 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS A,SUM(CASE WHEN YEAR_WEEK = 2 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS B,
            SUM(CASE WHEN YEAR_WEEK = 3 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS C,SUM(CASE WHEN YEAR_WEEK = 4 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS D,
            SUM(CASE WHEN YEAR_WEEK = 5 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS E,SUM(CASE WHEN YEAR_WEEK = 6 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS F,
            SUM(CASE WHEN YEAR_WEEK = 7 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS G,SUM(CASE WHEN YEAR_WEEK = 8 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS H,
            SUM(CASE WHEN YEAR_WEEK = 9 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS I,SUM(CASE WHEN YEAR_WEEK = 10 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS J,
            SUM(CASE WHEN YEAR_WEEK = 11 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS K,SUM(CASE WHEN YEAR_WEEK = 12 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS L,
            SUM(CASE WHEN YEAR_WEEK = 13 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS M,SUM(CASE WHEN YEAR_WEEK = 14 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS N,
            SUM(CASE WHEN YEAR_WEEK = 15 THEN WOW_YT_VIEWS_GROWTH ELSE 0 END) AS O,SUM(CASE WHEN YEAR_WEEK = 1 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS P, -- END OF YT
            SUM(CASE WHEN YEAR_WEEK = 2 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS Q,SUM(CASE WHEN YEAR_WEEK = 3 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS R,
            SUM(CASE WHEN YEAR_WEEK = 4 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS S,SUM(CASE WHEN YEAR_WEEK = 5 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS T,
            SUM(CASE WHEN YEAR_WEEK = 6 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS U,SUM(CASE WHEN YEAR_WEEK = 7 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS V,
            SUM(CASE WHEN YEAR_WEEK = 8 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS W,SUM(CASE WHEN YEAR_WEEK = 9 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS X,
            SUM(CASE WHEN YEAR_WEEK = 10 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS Y,SUM(CASE WHEN YEAR_WEEK = 11 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS Z,
            SUM(CASE WHEN YEAR_WEEK = 12 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS AA,SUM(CASE WHEN YEAR_WEEK = 13 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS BB,
            SUM(CASE WHEN YEAR_WEEK = 14 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS CC,SUM(CASE WHEN YEAR_WEEK = 15 THEN WOW_SPOTIFY_PLAYS_GROWTH ELSE 0 END) AS DD,  -- END OF SPOTIFY
            SUM(CASE WHEN YEAR_WEEK = 1 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS EE,
            SUM(CASE WHEN YEAR_WEEK = 2 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS FF,
            SUM(CASE WHEN YEAR_WEEK = 3 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS GG,
            SUM(CASE WHEN YEAR_WEEK = 4 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS HH,
            SUM(CASE WHEN YEAR_WEEK = 5 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS II,
            SUM(CASE WHEN YEAR_WEEK = 6 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS JJ,
            SUM(CASE WHEN YEAR_WEEK = 7 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS KK,
            SUM(CASE WHEN YEAR_WEEK = 8 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS LL,
            SUM(CASE WHEN YEAR_WEEK = 9 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS MM,
            SUM(CASE WHEN YEAR_WEEK = 10 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS NN,
            SUM(CASE WHEN YEAR_WEEK = 11 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS OO,
            SUM(CASE WHEN YEAR_WEEK = 12 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS PP,
            SUM(CASE WHEN YEAR_WEEK = 13 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS QQ,
            SUM(CASE WHEN YEAR_WEEK = 14 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS RR,
            SUM(CASE WHEN YEAR_WEEK = 15 THEN WOW_TIKTOK_POSTS_GROWTH ELSE 0 END) AS SS
     FROM (
           SELECT * 
           FROM (
                 SELECT CM_TRACK, CREATED_AT, SUM(YOUTUBE_VIEWS) AS YT_VIEWS,
                 SUM(YOUTUBE_VIEWS) - LAG(SUM(YOUTUBE_VIEWS), 7) OVER (ORDER BY CREATED_AT) AS WOW_YT_VIEWS_GROWTH,
                 SUM(SPOTIFY_PLAYS) - LAG(SUM(SPOTIFY_PLAYS), 7) OVER (ORDER BY CREATED_AT) AS WOW_SPOTIFY_PLAYS_GROWTH,
                 SUM(TIKTOK_POSTS) - LAG(SUM(TIKTOK_POSTS), 7) OVER (ORDER BY CREATED_AT) AS WOW_TIKTOK_POSTS_GROWTH,                  
                 1 + (CREATED_AT - DATE_TRUNC('year', CREATED_AT)) / 7 as YEAR_WEEK
                 FROM "CHARTMETRIC"."SHARES"."CAPSTONE_TRACK_STATS" AS TS
                 WHERE CREATED_AT BETWEEN '2021-01-01' AND '2021-12-31'
                 GROUP BY CREATED_AT, CM_TRACK
                 ORDER BY 2
                )       
           WHERE YEAR_WEEK = ROUND(YEAR_WEEK, 0))
       GROUP BY CM_TRACK, CREATED_AT
       ORDER BY CREATED_AT)
GROUP BY CM_TRACK
)

SELECT *
FROM BASE
LEFT JOIN QUARTERS_FINAL USING (CM_TRACK)
LEFT JOIN MOM_FINAL USING(CM_TRACK)
LEFT JOIN WOW_FINAL USING(CM_TRACK)

"""


df=pd.read_sql(q1, conn)
df

/Users/angelikalin/opt/anaconda3/lib/python3.9/site-packages/pandas/io/sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy
  warnings.warn(


### Ramdomly sample out 50k songs 
df_sample = df.sample(n=50000, random_state=123)
df_sample


### Create function to replace COUNTRY_NAME with COUNTRY_ID 

def country_identity(df):
    
    country = df['COUNTRY_NAME'].to_numpy()
    countries_list = []
    
    for x in country:
        if x not in countries_list:
            countries_list.append(x)
    
    #Create id starting at 1000
    df_country_name = df['COUNTRY_NAME']
    df_country_id = np.arange(1000, 1000 + len(df.COUNTRY_NAME.unique()),1)
    #replace 
    df['COUNTRY_NAME'].replace(countries_list, df_country_id, inplace = True)
    return(df)

country_identity(df_sample)


### Reduce cardinality for genre 

from collections import Counter
def cumulatively_categorise(column,threshold=0.75,return_categories_list=True):
  #Find the threshold value using the percentage and number of instances in the column
  threshold_value=int(threshold*len(column))
  #Initialise an empty list for our new minimised categories
  categories_list=[]
  #Initialise a variable to calculate the sum of frequencies
  s=0
  #Create a counter dictionary of the form unique_value: frequency
  counts=Counter(column)

  #Loop through the category name and its corresponding frequency after sorting the categories by descending order of frequency
  for i,j in counts.most_common():
    #Add the frequency to the global sum
    s+=dict(counts)[i]
    #Append the category name to the list
    categories_list.append(i)
    #Check if the global sum has reached the threshold value, if so break the loop
    if s>=threshold_value:
      break
  #Append the category Other to the list
  categories_list.append('Other')

  #Replace all instances not in our new categories by Other  
  new_column=column.apply(lambda x: x if x in categories_list else 'Other')

  #Return transformed column and unique values if return_categories=True
  if(return_categories_list):
    return new_column,categories_list
  #Return only the transformed column if return_categories=False
  else:
    return new_column


#Call the function with a default threshold of 75%
transformed_column,new_category_list=cumulatively_categorise(df_sample['ARTIST_GENRE'],return_categories_list=True)

df_sample['genre_new'] = transformed_column
df_sample.genre_new.value_counts()

Other          11611
Pop             7733
Rock            5377
Hip-Hop/Rap     5187
Latin           2524
Electronic      2299
Alternative     2270
R&B/Soul        2201
Dance           1503
J-Pop           1481
Country         1443
Jazz            1236
House           1123
Latin Pop       1110
Name: genre_new, dtype: int64


### Hot encode Genre 
def its_hot(df):
    genre_dummies = pd.get_dummies(df.genre_new)
    df = pd.concat([df, genre_dummies], axis=1)
    return df 

df_sample = its_hot(df_sample)


pd.set_option('display.max_columns', None)
df_sample


### Remove generic columns 
df_sample=df_sample.loc[:, df_sample.columns.drop(['CM_TRACK','CM_ALBUM','ALBUM_NAME','RELEASE_DATE',
                                            'ARTIST_NAME', 'ARTIST_COUNTRY_CODE2', 'GENRE_ID',
                                              'ARTIST_GENRE' ,'DUPE', 'GENRE', 'genre_new'])]


### Remove any NaNs 

def clean_dataset(df):
    df = df.set_index('TRACK_NAME')
    assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
    df.dropna(inplace=True)
    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)
    return df[indices_to_keep].astype(np.float64)

df_sample = clean_dataset(df_sample)


### Scale data 
#df_sample = df_sample.set_index('TRACK_NAME')
from sklearn.preprocessing import StandardScaler #used for 'Feature Scaling'
scaler = StandardScaler()
df_sample_scaled = pd.DataFrame(scaler.fit_transform(df_sample), index=df_sample.index, columns=df_sample.columns)
display('original',df_sample.head(2),'scaled',df_sample_scaled.head(2))

'original'

'scaled'


### Slice out WoW Columns 

qoq_youtube = df_sample_scaled[["Q1_YOUTUBE_VIEWS_GROWTH", "Q2_YOUTUBE_VIEWS_GROWTH", "Q3_YOUTUBE_VIEWS_GROWTH",
                       "Q4_YOUTUBE_VIEWS_GROWTH"]]


from sklearn.cluster import KMeans 
from sklearn.metrics import silhouette_samples, silhouette_score 
import time 

n_clusters = [3,4,5,6]

for i in n_clusters:
    start = time.time()
    clusterer = KMeans(n_clusters = i, random_state = 10)
    cluster_labels = clusterer.fit_predict(qoq_youtube)
    silhouette_avg = silhouette_score(qoq_youtube, cluster_labels)
    end = time.time()
    timetaken = end - start
    print("For n_clusters =", i, "The average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken)
    sample_silhouette_values = silhouette_samples(qoq_youtube, cluster_labels)

For n_clusters = 3 The average silhouette score is: 0.9945894587732068
This iteration took 21.140565156936646
For n_clusters = 4 The average silhouette score is: 0.9927833907607675
This iteration took 21.17417597770691
For n_clusters = 5 The average silhouette score is: 0.9816392760220527
This iteration took 21.210541009902954
For n_clusters = 6 The average silhouette score is: 0.9821774995554735
This iteration took 21.623109102249146


### First, determine EPS range 

from sklearn.neighbors import NearestNeighbors
from matplotlib import pyplot as plt


neighbors = NearestNeighbors(n_neighbors=8)
neighbors_fit = neighbors.fit(qoq_youtube)
distances, indices = neighbors_fit.kneighbors(qoq_youtube)

distances = np.sort(distances, axis=0)
distances = distances[:,1]
plt.axis([0, 51000, 0, 0.02])
plt.plot(distances)

[<matplotlib.lines.Line2D at 0x7f915830a8b0>]


### Determine Optimum EPS 
from sklearn.cluster import DBSCAN    
    
eps_range = [0.001, 0.002, 0.003, 0.004, 0.005]

for i in eps_range:
    start = time.time()
    print("eps value is" + str(i))
    db = DBSCAN(eps = i, min_samples=8).fit(qoq_youtube)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(qoq_youtube, labels)
    print(set(labels))
    silhouette_avg = silhouette_score(qoq_youtube, labels)
    end = time.time()
    timetaken = end - start
    print("For eps value ="+ str(i), labels, "the average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken, "seconds")

eps value is0.001
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1}
For eps value =0.001 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7571481334695498
This iteration took 68.994784116745 seconds
eps value is0.002
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, -1}
For eps value =0.002 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7402907798941708
This iteration took 70.48721098899841 seconds
eps value is0.003
{0, 1, 2, 3, 4, 5, 6, -1}
For eps value =0.003 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7841014590627013
This iteration took 71.33101677894592 seconds
eps value is0.004
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, -1}
For eps value =0.004 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7649543460662273
This iteration took 72.07980418205261 seconds
eps value is0.005
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1}
For eps value =0.005 [ 0  0  0 ...  0  0 -1] the average silhouette score is: 0.7708989318835603
This iteration took 72.89665389060974 seconds


min_samples = [7,8,9,10,11,12,13]

for i in min_samples: 
    start = time.time()
    db = DBSCAN(eps = 0.003, min_samples=i).fit(qoq_youtube)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(qoq_youtube, labels)
    end = time.time()
    timetaken = end - start
    
    print("For min_sample value =" +str(i), "clusters produced ="+ str(len(set(labels))))
    print("This iteration took", timetaken, "seconds")

For min_sample value =7 clusters produced =10
This iteration took 50.234596967697144 seconds
For min_sample value =8 clusters produced =8
This iteration took 50.025941133499146 seconds
For min_sample value =9 clusters produced =9
This iteration took 50.171427965164185 seconds
For min_sample value =10 clusters produced =7
This iteration took 50.31635308265686 seconds
For min_sample value =11 clusters produced =6
This iteration took 50.90813708305359 seconds
For min_sample value =12 clusters produced =6
This iteration took 49.9834508895874 seconds
For min_sample value =13 clusters produced =5
This iteration took 50.46366095542908 seconds


clustering = DBSCAN(eps=0.003, min_samples=12).fit(qoq_youtube)
components = clustering.components_
labels =clustering.labels_
core_sample_indices = clustering.core_sample_indices_                      

qoq_youtube.insert(0, 'cluster',clustering.labels_)
### count values within clusters 
qoq_youtube.cluster.value_counts()

 0    43532
-1     5262
 1       39
 3       17
 4       12
 2       12
Name: cluster, dtype: int64


### slice out the data we want 
df_no_zero_youtube = qoq_youtube.query('cluster == -1' or 'cluster == 1' or 'cluster == 2' or 'cluster == 3' or 'cluster == 4')
### drop cluster column
df_no_zero_youtube = df_no_zero_youtube.drop(['cluster'], axis = 1)


### Determine optimum Epsilon 

from sklearn.neighbors import NearestNeighbors
from matplotlib import pyplot as plt


neighbors = NearestNeighbors(n_neighbors=8)
neighbors_fit = neighbors.fit(df_no_zero_youtube)
distances, indices = neighbors_fit.kneighbors(df_no_zero_youtube)

distances = np.sort(distances, axis=0)
distances = distances[:,1]
plt.axis([0, 5300, 0, 1])
plt.plot(distances)

[<matplotlib.lines.Line2D at 0x7f8a980ce5e0>]


n_clusters = [3,4,5,6]

for i in n_clusters:
    clusterer = KMeans(n_clusters = i, random_state = 10)
    cluster_labels = clusterer.fit_predict(df_no_zero_youtube)
    silhouette_avg = silhouette_score(df_no_zero_youtube, cluster_labels)
    
    print("For n_clusters =", i, "The average silhouette score is:", silhouette_avg)
    
    sample_silhouette_values = silhouette_samples(df_no_zero_youtube, cluster_labels)

For n_clusters = 3 The average silhouette score is: 0.9379727354949758
For n_clusters = 4 The average silhouette score is: 0.9408528351853975
For n_clusters = 5 The average silhouette score is: 0.8476584343370819
For n_clusters = 6 The average silhouette score is: 0.8542504814108389


eps_range = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]

for i in eps_range:
    start = time.time()
    print("eps value is" + str(i))
    db = DBSCAN(eps = i, min_samples=8).fit(df_no_zero_youtube)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(df_no_zero_youtube, labels)
    print(set(labels))
    silhouette_avg = silhouette_score(df_no_zero_youtube, labels)
    end = time.time()
    timetaken = start - end
    print("For eps value ="+ str(i), labels, "the average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken)

eps value is0.1
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, -1}
For eps value =0.1 [0 0 0 ... 0 0 0] the average silhouette score is: 0.04638665279312412
This iteration took -0.6213250160217285
eps value is0.2
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1}
For eps value =0.2 [0 0 0 ... 0 0 0] the average silhouette score is: 0.2870292358000253
This iteration took -0.6575460433959961
eps value is0.3
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, -1}
For eps value =0.3 [0 0 0 ... 0 0 0] the average silhouette score is: 0.34195394981649324
This iteration took -0.6816840171813965
eps value is0.4
{0, 1, 2, 3, 4, 5, 6, 7, -1}
For eps value =0.4 [0 0 0 ... 0 0 0] the average silhouette score is: 0.5240871644895779
This iteration took -0.7124831676483154
eps value is0.5
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1}
For eps value =0.5 [0 0 0 ... 0 0 0] the average silhouette score is: 0.468714986437441
This iteration took -0.7362239360809326
eps value is0.6
{0, 1, 2, 3, 4, -1}
For eps value =0.6 [0 0 0 ... 0 0 0] the average silhouette score is: 0.5605654989465374
This iteration took -0.7342960834503174
eps value is0.7
{0, 1, 2, 3, 4, -1}
For eps value =0.7 [0 0 0 ... 0 0 0] the average silhouette score is: 0.576257889484914
This iteration took -0.7478110790252686


min_samples = [7,8,9,10,11,12,13, 14, 15]

for i in min_samples: 
    db = DBSCAN(eps = 0.5, min_samples=i).fit(df_no_zero_youtube)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(df_no_zero_youtube, labels)
    
    print("For min_sample value =" +str(i), "clusters produced ="+ str(len(set(labels))))

For min_sample value =7 clusters produced =10
For min_sample value =8 clusters produced =11
For min_sample value =9 clusters produced =8
For min_sample value =10 clusters produced =8
For min_sample value =11 clusters produced =9
For min_sample value =12 clusters produced =7
For min_sample value =13 clusters produced =5
For min_sample value =14 clusters produced =5
For min_sample value =15 clusters produced =4


clustering = DBSCAN(eps=0.6, min_samples=15).fit(df_no_zero_youtube)
components = clustering.components_
labels =clustering.labels_
core_sample_indices = clustering.core_sample_indices_                      

df_no_zero_youtube.insert(0, 'cluster',clustering.labels_)
### count values within clusters 
df_no_zero_youtube.cluster.value_counts()

 0    4615
-1     567
 1      41
 3      21
 2      18
Name: cluster, dtype: int64


clusters = df_no_zero_youtube \
    .groupby('cluster') \
    .agg('mean')

import matplotlib.pyplot as plt
from matplotlib import colors

def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):
    rng = M - m
    norm = colors.Normalize(m - (rng * low),
                            M + (rng * high))
    normed = norm(s.values)
    c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
    return ['background-color: %s' % color for color in c]

clusters.style.apply(background_gradient,
               cmap='Wistia',
               m=clusters.min().min(),
               M=clusters.max().max())


import plotly.express as px

fig = px.scatter_3d(df_no_zero_youtube, x="Q1_YOUTUBE_VIEWS_GROWTH", y="Q2_YOUTUBE_VIEWS_GROWTH", z= 'Q3_YOUTUBE_VIEWS_GROWTH',
              color='cluster')
fig.show()
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))


### Investigate cluster -1 

df_negative_1 = df_no_zero_youtube.query('cluster == -1')
list_negative_1 = df_negative_1.index.tolist()

negative_1 = df.query("TRACK_NAME in @list_negative_1")
negative_1


negative_1.ARTIST_GENRE.value_counts()

Pop            138
Rock           111
Hip-Hop/Rap     93
Latin           91
R&B/Soul        51
              ... 
Opera            1
Arabic           1
Dancehall        1
Reggaeton        1
Others           1
Name: ARTIST_GENRE, Length: 61, dtype: int64


df_youtube_cn1 = negative_1.COUNTRY_NAME.value_counts()
df_youtube_cn1 = pd.DataFrame(data=df_youtube_cn1)
df_youtube_cn1.columns = ['count']
df_youtube_cn1.plot.bar()

<AxesSubplot:>


df_youtube_cn1_genre = negative_1.ARTIST_GENRE.value_counts()
df_youtube_cn1_genre = pd.DataFrame(data=df_youtube_cn1_genre)
df_youtube_cn1_genre.columns = ['count']
df_youtube_cn1_genre.plot.bar()

<AxesSubplot:>


### Investigate cluster 0

df_zero = df_no_zero_youtube.query('cluster == 0')
list_two = df_zero.index.tolist()

zero = df.query("TRACK_NAME in @list_two")
zero


df_youtube_c0 = zero.COUNTRY_NAME.value_counts()
df_youtube_c0 = pd.DataFrame(data=df_youtube_c0)
df_youtube_c0.columns = ['count']
df_youtube_c0.plot.bar()

<AxesSubplot:>

/Users/angelikalin/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning:

Glyph 146 (\x92) missing from current font.


df_youtube_c0_genre = zero.ARTIST_GENRE.value_counts()
df_youtube_c0_genre = pd.DataFrame(data=df_youtube_c0_genre)
df_youtube_c0_genre.columns = ['count']
df_youtube_c0_genre.plot.bar()

<AxesSubplot:>


### Investigate cluster 1

df_one = df_no_zero_youtube.query('cluster == 1')
list_one = df_one.index.tolist()

one = df.query("TRACK_NAME in @list_one")
one


df_youtube_c1 = one.COUNTRY_NAME.value_counts()
df_youtube_c1 = pd.DataFrame(data=df_youtube_c1)
df_youtube_c1.columns = ['count']
df_youtube_c1.plot.bar()

<AxesSubplot:>


df_youtube_c1_genre = one.ARTIST_GENRE.value_counts()
df_youtube_c1_genre = pd.DataFrame(data=df_youtube_c1_genre)
df_youtube_c1_genre.columns = ['count']
df_youtube_c1_genre.plot.bar()

<AxesSubplot:>


### Investigate cluster 2

df_2 = df_no_zero_youtube.query('cluster == 2')
list_2 = df_2.index.tolist()

two = df.query("TRACK_NAME in @list_2")
two


df_youtube_c2_country = two.COUNTRY_NAME.value_counts()
df_youtube_c2_country = pd.DataFrame(data=df_youtube_c2_country)
df_youtube_c2_country.columns = ['count']
df_youtube_c2_country.plot.bar()

<AxesSubplot:>


df_youtube_c2 = two.ARTIST_GENRE.value_counts()
df_youtube_c2 = pd.DataFrame(data=df_youtube_c2)
df_youtube_c2.columns = ['count']
df_youtube_c2.plot.bar()

<AxesSubplot:>


### Investigate cluster 3

df_3 = df_no_zero_youtube.query('cluster == 3')
list_3 = df_3.index.tolist()

three = df.query("TRACK_NAME in @list_3")
three


df_youtube_c3_country = three.COUNTRY_NAME.value_counts()
df_youtube_c3_country = pd.DataFrame(data=df_youtube_c2_country)
df_youtube_c3_country.columns = ['count']
df_youtube_c3_country.plot.bar()

<AxesSubplot:>


df_youtube_c3_genre = three.ARTIST_GENRE.value_counts()
df_youtube_c3_genre = pd.DataFrame(data=df_youtube_c3_genre)
df_youtube_c3_genre.columns = ['count']
df_youtube_c3_genre.plot.bar()

<AxesSubplot:>


### Slice out WoW Columns 

qoq_spotify = df_sample_scaled[["Q1_SPOTIFY_PLAYS_GROWTH", "Q2_SPOTIFY_PLAYS_GROWTH", "Q3_SPOTIFY_PLAYS_GROWTH",
                       "Q4_SPOTIFY_PLAYS_GROWTH"]]


from sklearn.cluster import KMeans 
from sklearn.metrics import silhouette_samples, silhouette_score 
import time 

n_clusters = [3,4,5,6]

for i in n_clusters:
    start = time.time()
    clusterer = KMeans(n_clusters = i, random_state = 10)
    cluster_labels = clusterer.fit_predict(qoq_spotify)
    silhouette_avg = silhouette_score(qoq_spotify, cluster_labels)
    end = time.time()
    timetaken = end - start
    print("For n_clusters =", i, "The average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken)
    sample_silhouette_values = silhouette_samples(qoq_youtube, cluster_labels)

For n_clusters = 3 The average silhouette score is: 0.9967500853243395
This iteration took 21.889666080474854
For n_clusters = 4 The average silhouette score is: 0.9941777959767116
This iteration took 21.822834968566895
For n_clusters = 5 The average silhouette score is: 0.9935838383820632
This iteration took 21.408591270446777
For n_clusters = 6 The average silhouette score is: 0.9748434589216639
This iteration took 21.693091869354248


### First, determine EPS range 

from sklearn.neighbors import NearestNeighbors
from matplotlib import pyplot as plt


neighbors = NearestNeighbors(n_neighbors=8)
neighbors_fit = neighbors.fit(qoq_spotify)
distances, indices = neighbors_fit.kneighbors(qoq_spotify)

distances = np.sort(distances, axis=0)
distances = distances[:,1]
plt.axis([0, 51000, 0, 0.02])
plt.plot(distances)

[<matplotlib.lines.Line2D at 0x7f8a192090a0>]


### Determine Optimum EPS 
from sklearn.cluster import DBSCAN    
    
eps_range = [0.001, 0.002, 0.003, 0.004, 0.005]

for i in eps_range:
    start = time.time()
    print("eps value is" + str(i))
    db = DBSCAN(eps = i, min_samples=8).fit(qoq_spotify)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(qoq_spotify, labels)
    print(set(labels))
    silhouette_avg = silhouette_score(qoq_spotify, labels)
    end = time.time()
    timetaken = end - start
    print("For eps value ="+ str(i), labels, "the average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken, "seconds")

eps value is0.001
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, -1}
For eps value =0.001 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.15876002626427932
This iteration took 61.006664991378784 seconds
eps value is0.002
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, -1}
For eps value =0.002 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.2918826649922619
This iteration took 60.876980781555176 seconds
eps value is0.003
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, -1}
For eps value =0.003 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.32459875798168575
This iteration took 62.36633014678955 seconds
eps value is0.004
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, -1}
For eps value =0.004 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.421031849056015
This iteration took 63.780704975128174 seconds
eps value is0.005
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, -1}
For eps value =0.005 [ 0 -1  0 ...  0  0  0] the average silhouette score is: 0.5022656507002081
This iteration took 62.979327917099 seconds


min_samples = [24,25,26,27,28]

for i in min_samples: 
    start = time.time()
    db = DBSCAN(eps = 0.004, min_samples=i).fit(qoq_spotify)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(qoq_spotify, labels)
    end = time.time()
    timetaken = end - start
    
    print("For min_sample value =" +str(i), "clusters produced ="+ str(len(set(labels))))
    print("This iteration took", timetaken, "seconds")

For min_sample value =24 clusters produced =9
This iteration took 41.484431982040405 seconds
For min_sample value =25 clusters produced =11
This iteration took 40.71910285949707 seconds
For min_sample value =26 clusters produced =11
This iteration took 40.97695708274841 seconds
For min_sample value =27 clusters produced =8
This iteration took 41.1752188205719 seconds
For min_sample value =28 clusters produced =8
This iteration took 40.77605080604553 seconds


clustering = DBSCAN(eps=0.004, min_samples=28).fit(qoq_spotify)
components = clustering.components_
labels =clustering.labels_
core_sample_indices = clustering.core_sample_indices_                      

qoq_spotify.insert(0, 'cluster',clustering.labels_)
### count values within clusters 
qoq_spotify.cluster.value_counts()

 0    42752
-1     5669
 1      178
 2      119
 4       61
 5       35
 3       32
 6       28
Name: cluster, dtype: int64


### slice out the data we want 
df_no_zero_spotify = qoq_spotify.query('cluster == -1' or 'cluster == 1' or 'cluster == 2' or 'cluster == 3' or 'cluster == 4')
### drop cluster column
df_no_zero_spotify = df_no_zero_spotify.drop(['cluster'], axis = 1)


### Determine optimum Epsilon range

from sklearn.neighbors import NearestNeighbors
from matplotlib import pyplot as plt


neighbors = NearestNeighbors(n_neighbors=8)
neighbors_fit = neighbors.fit(df_no_zero_spotify)
distances, indices = neighbors_fit.kneighbors(df_no_zero_spotify)

distances = np.sort(distances, axis=0)
distances = distances[:,1]
plt.axis([0, 6400, 0, 1])
plt.plot(distances)

[<matplotlib.lines.Line2D at 0x7f90f80c0e50>]


### Determine optimum EPS 

eps_range = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3]

for i in eps_range:
    start = time.time()
    print("eps value is" + str(i))
    db = DBSCAN(eps = i, min_samples=8).fit(df_no_zero_spotify)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(df_no_zero_spotify, labels)
    print(set(labels))
    silhouette_avg = silhouette_score(df_no_zero_spotify, labels)
    end = time.time()
    timetaken = start - end
    print("For eps value ="+ str(i), labels, "the average silhouette score is:", silhouette_avg)
    print("This iteration took", timetaken)

eps value is0.05
{0, 1, 2, 3, -1}
For eps value =0.05 [0 0 0 ... 0 0 0] the average silhouette score is: 0.4993567007493581
This iteration took -0.767949104309082
eps value is0.1
{0, 1, 2, 3, 4, 5, 6, -1}
For eps value =0.1 [0 0 0 ... 0 0 0] the average silhouette score is: -0.005111500251722316
This iteration took -0.667341947555542
eps value is0.15
{0, 1, 2, 3, 4, -1}
For eps value =0.15 [0 0 0 ... 0 0 0] the average silhouette score is: 0.4948063068861464
This iteration took -0.6944882869720459
eps value is0.2
{0, 1, 2, 3, 4, -1}
For eps value =0.2 [0 0 0 ... 0 0 0] the average silhouette score is: 0.6340161331143919
This iteration took -0.7002310752868652
eps value is0.25
{0, 1, 2, 3, 4, -1}
For eps value =0.25 [0 0 0 ... 0 0 0] the average silhouette score is: 0.6969086195838394
This iteration took -0.7103168964385986
eps value is0.3
{0, 1, 2, 3, 4, -1}
For eps value =0.3 [0 0 0 ... 0 0 0] the average silhouette score is: 0.6922544906937437
This iteration took -0.7114670276641846


### Determine optimum min_samples

min_samples = [7,8,9,10,11,12,13, 14, 15]

for i in min_samples: 
    db = DBSCAN(eps = 0.3, min_samples=i).fit(df_no_zero_spotify)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True 
    labels = db.labels_
    silhouette_avg = silhouette_score(df_no_zero_spotify, labels)
    
    print("For min_sample value =" +str(i), "clusters produced ="+ str(len(set(labels))))

For min_sample value =7 clusters produced =8
For min_sample value =8 clusters produced =6
For min_sample value =9 clusters produced =5
For min_sample value =10 clusters produced =4
For min_sample value =11 clusters produced =5
For min_sample value =12 clusters produced =5
For min_sample value =13 clusters produced =3
For min_sample value =14 clusters produced =3
For min_sample value =15 clusters produced =3


clustering = DBSCAN(eps=0.3, min_samples=9).fit(df_no_zero_spotify)
components = clustering.components_
labels =clustering.labels_
core_sample_indices = clustering.core_sample_indices_                      

df_no_zero_spotify.insert(0, 'cluster',clustering.labels_)
### count values within clusters 
df_no_zero_spotify.cluster.value_counts()

 0    5429
-1     202
 1      16
 3      13
 2       9
Name: cluster, dtype: int64


clusters = df_no_zero_spotify \
    .groupby('cluster') \
    .agg('mean')

import matplotlib.pyplot as plt
from matplotlib import colors

def background_gradient(s, m, M, cmap='PuBu', low=0, high=0):
    rng = M - m
    norm = colors.Normalize(m - (rng * low),
                            M + (rng * high))
    normed = norm(s.values)
    c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
    return ['background-color: %s' % color for color in c]

clusters.style.apply(background_gradient,
               cmap='Wistia',
               m=clusters.min().min(),
               M=clusters.max().max())


import plotly.express as px

fig = px.scatter_3d(df_no_zero_spotify, x="Q1_SPOTIFY_PLAYS_GROWTH", y="Q2_SPOTIFY_PLAYS_GROWTH", z= 'Q3_SPOTIFY_PLAYS_GROWTH',
              color='cluster')
fig.show()
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))


### Investigate cluster -1 

df_negative_1_s = df_no_zero_spotify.query('cluster == -1')
list_negative_1 = df_negative_1.index.tolist()

negative_1_s = df.query("TRACK_NAME in @list_negative_1")
negative_1_s


negative_1_s.ARTIST_GENRE.value_counts()

Pop                   83
Rock                  49
R&B/Soul              29
Hip-Hop/Rap           27
Country               21
Electronic            20
Dance                 16
Alternative           16
Latin                 15
Metal                  9
House                  8
Latin Pop              8
Classical              7
Jazz                   7
Vocal                  6
Hard Rock              6
Holiday                4
Soft Rock              4
Christian              4
Indie Rock             3
Blues                  3
Latin Rock             2
Reggae                 2
Punk                   2
Latin Hip-Hop/Rap      2
Singer/Songwriter      2
Easy Listening         2
Children's Music       2
Musicals               2
Others                 1
Forro                  1
African                1
Schlager               1
J-Pop                  1
Grunge                 1
Indian Hip-Hop/Rap     1
Techno                 1
K-Pop                  1
New Wave               1
Swing                  1
New Age                1
Classic Rock           1
Salsa                  1
Sertanejo              1
Name: ARTIST_GENRE, dtype: int64


df_spotify_cn1_country = negative_1_s.COUNTRY_NAME.value_counts()
df_spotify_cn1_country = pd.DataFrame(data=df_spotify_cn1_country)
df_spotify_cn1_country.columns = ['count']
df_spotify_cn1_country.plot.bar()

<AxesSubplot:>


df_spotify_cn1_genre = negative_1_s.ARTIST_GENRE.value_counts()
df_spotify_cn1_genre = pd.DataFrame(data=df_youtube_c1_genre)
df_spotify_cn1_genre.columns = ['count']
df_spotify_cn1_genre.plot.bar()

<AxesSubplot:>


### Investigate cluster 0

df_0 = df_no_zero_spotify.query('cluster == 0')
list_0 = df_0.index.tolist()

zero_s = df.query("TRACK_NAME in @list_0")
zero_s


df_spotify_c0 = zero_s.COUNTRY_NAME.value_counts()
df_spotify_c0 = pd.DataFrame(data=df_spotify_c0)
df_spotify_c0.columns = ['count']
df_spotify_c0.plot.bar()

<AxesSubplot:>


df_spotify_c0_genre = zero_s.ARTIST_GENRE.value_counts()
df_spotify_c0_genre = pd.DataFrame(data=df_spotify_c0_genre)
df_spotify_c0_genre.columns = ['count']
df_spotify_c0_genre.plot.bar()

<AxesSubplot:>


### Investigate cluster 1

df_1_s = df_no_zero_spotify.query('cluster == 1')
list_1_s = df_1_s.index.tolist()

one_s = df.query("TRACK_NAME in @list_1_s")
one_s.head()


df_spotify_c1 = one_s.COUNTRY_NAME.value_counts()
df_spotify_c1 = pd.DataFrame(data=df_spotify_c1)
df_spotify_c1.columns = ['count']
df_spotify_c1.plot.bar()

<AxesSubplot:>


df_spotify_c1_genre = one_s.ARTIST_GENRE.value_counts()
df_spotify_c1_genre = pd.DataFrame(data=df_spotify_c1_genre)
df_spotify_c1_genre.columns = ['count']
df_spotify_c1_genre.plot.bar()

<AxesSubplot:>


### Investigate cluster 2

df_2_s = df_no_zero_spotify.query('cluster == 2')
list_2_s = df_2_s.index.tolist()

two_s = df.query("TRACK_NAME in @list_2_s")
two_s.head()


df_spotify_c2 = two_s.COUNTRY_NAME.value_counts()
df_spotify_c2 = pd.DataFrame(data=df_spotify_c2)
df_spotify_c2.columns = ['count']
df_spotify_c2.plot.bar()

<AxesSubplot:>


df_spotify_c2_genre = two_s.ARTIST_GENRE.value_counts()
df_spotify_c2_genre = pd.DataFrame(data=df_spotify_c2_genre)
df_spotify_c2.columns = ['count']
df_spotify_c2.plot.bar()

<AxesSubplot:>


### Investigate cluster 3

df_3_s = df_no_zero_spotify.query('cluster == 3')
list_3_s = df_3_s.index.tolist()

three_s = df.query("TRACK_NAME in @list_3_s")
three_s.head()


df_spotify_c3 = three_s.COUNTRY_NAME.value_counts()
df_spotify_c3 = pd.DataFrame(data=df_spotify_c3)
df_spotify_c3.columns = ['count']
df_spotify_c3.plot.bar()

<AxesSubplot:>


df_spotify_c3_genre = three_s.ARTIST_GENRE.value_counts()
df_spotify_c3_genre = pd.DataFrame(data=df_spotify_c3_genre)
df_spotify_c3_genre.columns = ['count']
df_spotify_c3_genre.plot.bar()

<AxesSubplot:>

	COUNTRY_NAME	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_PLAYS_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_PLAYS_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	M1_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M1_YOUTUBE_LIKES_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M1_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_PLAYS_GROWTH	M3_SPOTIFY_PLAYS_GROWTH	M4_SPOTIFY_PLAYS_GROWTH	M5_SPOTIFY_PLAYS_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M1_SPOTIFY_POPULARITY_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M1_TIKTOK_POSTS_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M1_AIRPLAY_STREAMS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK1_SPOTIFY_PLAYS_GROWTH	WK2_SPOTIFY_PLAYS_GROWTH	WK3_SPOTIFY_PLAYS_GROWTH	WK4_SPOTIFY_PLAYS_GROWTH	WK5_SPOTIFY_PLAYS_GROWTH	WK6_SPOTIFY_PLAYS_GROWTH	WK7_SPOTIFY_PLAYS_GROWTH	WK8_SPOTIFY_PLAYS_GROWTH	WK9_SPOTIFY_PLAYS_GROWTH	WK10_SPOTIFY_PLAYS_GROWTH	WK11_SPOTIFY_PLAYS_GROWTH	WK12_SPOTIFY_PLAYS_GROWTH	WK13_SPOTIFY_PLAYS_GROWTH	WK14_SPOTIFY_PLAYS_GROWTH	WK15_SPOTIFY_PLAYS_GROWTH	WK1_TIKTOK_POSTS_GROWTH	WK2_TIKTOK_POSTS_GROWTH	WK3_TIKTOK_POSTS_GROWTH	WK4_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH	Alternative	Country	Dance	Electronic	Hip-Hop/Rap	House	J-Pop	Jazz	Latin	Latin Pop	Other	Pop	R&B/Soul	Rock
TRACK_NAME
Te Prefiero Lejos	-0.801913	-0.066995	-0.078786	-0.028792	-0.588250	-0.096746	-0.038017	-0.01681	-0.017012	-0.014578	-0.512871	-0.082558	-0.082867	-0.010637	-0.019187	-0.040787	0.322898	0.047209	-0.061612	-0.024661	-0.022676	-0.115446	0.380679	-0.099082	-0.086349	0.0	-0.045817	-0.063735	-0.066255	-0.064269	-0.071527	-0.036888	-0.038715	-0.046073	-0.039754	-0.045333	-0.048384	0.0	-0.047626	-0.063571	-0.061336	-0.063292	-0.055601	-0.031456	-0.041776	-0.052006	-0.042994	-0.038376	-0.043929	0.0	-0.028637	-0.028579	-0.028214	-0.028046	-0.153532	-0.151827	-0.103864	-0.019720	-0.127255	-0.022176	-0.101869	0.0	-0.239679	-0.311199	-0.238074	-0.284444	-0.271678	-0.235229	-0.242684	-0.26056	-0.003946	-0.254593	0.708040	0.0	-0.054837	-0.088185	-0.084232	-0.090177	-0.097749	-0.082555	-0.108085	-0.074872	-0.078152	-0.093411	-0.084281	0.0	-0.032617	-0.030574	-0.048919	-0.112104	-0.10608	-0.053721	-0.067381	-0.069875	-0.090428	-0.08831	-0.081067	0.002849	0.021138	0.023017	0.028546	0.036744	0.023692	0.023985	0.028802	0.037078	0.045087	0.042905	0.045856	0.035091	0.042129	0.036897	0.007607	0.003512	0.005857	0.00048	0.008648	-0.002804	0.005426	-0.001721	0.000358	-0.000742	0.008724	0.006673	0.004161	0.005506	0.009518	0.005755	0.009826	0.009818	0.009259	0.01862	0.015899	0.016524	0.022105	0.02297	0.021076	0.021456	0.014533	0.017734	0.023244	0.02306	-0.219166	-0.173361	-0.176035	-0.218807	-0.337095	-0.151033	-0.174733	-0.160541	-0.229381	-0.150181	1.810576	-0.428909	-0.214563	-0.348573
Pour Out A Little Liquor	-0.713816	-0.066995	-0.078786	-0.028792	2.208278	0.029257	-0.036984	-0.01681	-0.017012	-0.014578	0.051900	-0.006332	-0.082867	-0.010637	-0.019187	0.078347	-1.886162	0.047209	-0.061612	-0.024661	-0.022676	0.188656	3.552841	-0.099082	-0.086349	0.0	-0.045817	-0.063735	-0.066255	-0.064269	-0.071527	-0.036888	-0.038715	-0.046073	-0.039754	-0.045333	-0.048384	0.0	-0.047626	-0.063571	-0.061336	-0.063292	-0.055601	-0.031456	-0.041776	-0.052006	-0.042994	-0.038376	-0.043929	0.0	-0.028637	-0.028579	-0.028214	-0.028046	0.314375	0.287159	0.185762	0.020282	0.199781	0.019807	0.086437	0.0	-0.239679	-0.214722	-0.238074	-0.165289	-0.271678	-0.235229	-0.242684	-0.26056	-0.173262	5.289707	-0.093385	0.0	0.126956	-0.007015	0.124935	-0.090177	-0.097749	0.154872	0.035391	-0.074872	-0.078152	-0.093411	-0.084281	0.0	-0.032617	-0.030574	-0.048919	-0.112104	-0.10608	-0.053721	-0.067381	-0.069875	-0.090428	-0.08831	-0.081067	0.002849	0.021138	0.023017	0.028546	0.036744	0.023692	0.023985	0.028802	0.037078	0.045087	0.042905	0.045856	0.035091	0.042129	0.036897	0.007607	0.003512	0.005857	0.00048	0.008648	-0.002804	0.005426	-0.001721	0.000358	-0.000742	0.008724	0.006673	0.004161	0.005506	0.009518	0.005755	0.009826	0.009818	0.009259	0.01862	0.015899	-0.017350	0.025456	0.02297	0.022307	0.023849	0.014533	0.017734	0.023244	0.02306	-0.219166	-0.173361	-0.176035	-0.218807	2.966527	-0.151033	-0.174733	-0.160541	-0.229381	-0.150181	-0.552310	-0.428909	-0.214563	-0.348573

	Q1_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH
cluster
-1	3.325468	0.167801	0.452928	1.564610
0	0.216701	0.080405	0.053391	0.058367
1	-0.021774	6.295263	0.146793	0.111615
2	0.287102	0.127791	-2.960023	-0.034340
3	0.482192	0.102507	0.136515	-4.207431

	Q1_SPOTIFY_PLAYS_GROWTH	Q2_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH
cluster
-1	6.166798	3.512660	2.898341	8.340313
0	-0.008941	-0.014578	0.186263	0.502784
1	2.963808	-0.014578	-0.040787	0.526614
2	-0.028792	-0.014578	3.078321	7.074816
3	-0.028792	-0.014578	2.856379	6.077754

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	...	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
0	11019719	Keep On Running	12498342	Sea of Approval (Deluxe)	2014-01-01	Andy Bull	AU	Australia	462887.0	Electronic	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	16011936	Willing And Able	1106967	Diamonds And Pearls	1991-01-01	Prince	US	United States	462884.0	Rock	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	11162739	Les Derniers Seront Les Premiers	711236	Original Album Classics	1990-01-01	Céline Dion	CA	Canada	462882.0	Pop	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	60414497	Calima	14685962	Calima - Single	2021-09-16	None	None	None	NaN	None	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	64718251	Symphony No. 39 in E-Flat Major, K. 543: IV. F...	15415581	Mozart: Symphonies Nos. 38 & 39	2021-10-22	NDR Radiophilharmonie	US	United States	462882.0	Pop	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
102540	34485129	Sad Song	8173893	Sad Song	2021-04-14	Hope Waidley	US	United States	462882.0	Pop	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102541	15813972	Valentine's Day	889361	Tunnel Of Love	1987-09-28	Bruce Springsteen	US	United States	462891.0	Singer/Songwriter	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102542	66304387	I Deserve (w/ NOS.)	18951649	Black Love Is...	2022-02-10	Smino	US	United States	462883.0	Hip-Hop/Rap	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102543	32822574	We Were Right	7475728	We Were Right	2021-02-12	AKA AKA	DE	Germany	462887.0	Electronic	...	0.0	0.0	-703.0	0.0	-535892.0	5.0	-1183.0	0.0	0.0	-649.0
102544	17119125	I'll Find My Way Home - Remastered	6939072	Christmas Chill	2020-10-30	Jon & Vangelis	GR	Greece	462882.0	Pop	...	0.0	0.0	0.0	0.0	0.0	-245.0	0.0	0.0	-18.0	-254.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	...	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
63119	28343556	Te Prefiero Lejos	5653976	Te Prefiero Lejos	2020-01-31	Ilegales	ES	Spain	462929.0	Punk	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
35359	15464089	Pour Out A Little Liquor	2939501	Wesside Classic, Vol. 1	2014-07-29	2Pac	US	United States	462883.0	Hip-Hop/Rap	...	0.0	-1793.0	162.0	0.0	136.0	177.0	0.0	0.0	0.0	0.0
46459	65313103	Pages (feat. Rook)	19843874	Last Day On Earth	2022-04-08	SK8	US	United States	462882.0	Pop	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
99290	12307342	Ma religion dans son regard	3435571	L'album de sa vie 50 titres	2018-09-28	Johnny Hallyday	FR	France	462882.0	Pop	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-23.0	0.0	0.0
295	48212874	QUEEN	11336333	FEMULINE	2021-06-08	Todrick Hall	US	United States	462883.0	Hip-Hop/Rap	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6343	11364916	Kapitel 14 - Der Weihnachtsabend (Folge 051)	1553845	Folge 51: Der Weihnachtsabend	1986-01-01	Benjamin Blümchen	DE	Germany	462932.0	Spoken Word	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
73522	11328739	Hei, Pippi Langstrumpf	224606	The Best of Generation Fernseh-Kult	2010-01-05	Pippi Langstrumpf	DE	Germany	462885.0	Dance	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
94075	20016819	Will You Sing	2650049	Heaven and Earth	2018-06-22	Kamasi Washington	US	United States	462893.0	Jazz	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
58013	15483779	I Belong To You	6159205	You Mean the World to Me: The Best Love Songs ...	2020-06-12	Toni Braxton	US	United States	462889.0	R&B/Soul	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-5535.0	0.0	-24349.0
18879	15782231	The Wexford Carol	63071	Songs of Joy & Peace	2008-10-10	Natalie MacMaster	CA	Canada	462903.0	Holiday	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	COUNTRY_NAME	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_PLAYS_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_PLAYS_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	M1_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M1_YOUTUBE_LIKES_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M1_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_PLAYS_GROWTH	M3_SPOTIFY_PLAYS_GROWTH	M4_SPOTIFY_PLAYS_GROWTH	M5_SPOTIFY_PLAYS_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M1_SPOTIFY_POPULARITY_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M1_TIKTOK_POSTS_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M1_AIRPLAY_STREAMS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK1_SPOTIFY_PLAYS_GROWTH	WK2_SPOTIFY_PLAYS_GROWTH	WK3_SPOTIFY_PLAYS_GROWTH	WK4_SPOTIFY_PLAYS_GROWTH	WK5_SPOTIFY_PLAYS_GROWTH	WK6_SPOTIFY_PLAYS_GROWTH	WK7_SPOTIFY_PLAYS_GROWTH	WK8_SPOTIFY_PLAYS_GROWTH	WK9_SPOTIFY_PLAYS_GROWTH	WK10_SPOTIFY_PLAYS_GROWTH	WK11_SPOTIFY_PLAYS_GROWTH	WK12_SPOTIFY_PLAYS_GROWTH	WK13_SPOTIFY_PLAYS_GROWTH	WK14_SPOTIFY_PLAYS_GROWTH	WK15_SPOTIFY_PLAYS_GROWTH	WK1_TIKTOK_POSTS_GROWTH	WK2_TIKTOK_POSTS_GROWTH	WK3_TIKTOK_POSTS_GROWTH	WK4_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH	Alternative	Country	Dance	Electronic	Hip-Hop/Rap	House	J-Pop	Jazz	Latin	Latin Pop	Other	Pop	R&B/Soul	Rock
TRACK_NAME
Te Prefiero Lejos	1000.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	-6.0	0.0	0.0	0.0	0.0	0.0	-2.0	0.0	0.0	0.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
Pour Out A Little Liquor	1001.0	0.0	0.0	0.0	49.0	25.0	1.0	0.0	0.0	0.0	0.0	10.0	0.0	0.0	0.0	456925.0	-35.0	0.0	0.0	0.0	0.0	512246.0	37.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	155876.0	145978.0	162369.0	148578.0	166433.0	161453.0	184360.0	0.0	0.0	2.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	35.0	1.0	0.0	17.0	5.0	13.0	0.0	0.0	14.0	16.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-1793.0	162.0	0.0	136.0	177.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_PLAYS_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_PLAYS_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M1_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M1_YOUTUBE_LIKES_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M1_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_PLAYS_GROWTH	M3_SPOTIFY_PLAYS_GROWTH	M4_SPOTIFY_PLAYS_GROWTH	M5_SPOTIFY_PLAYS_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M1_SPOTIFY_POPULARITY_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M1_TIKTOK_POSTS_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M1_AIRPLAY_STREAMS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK1_SPOTIFY_PLAYS_GROWTH	WK2_SPOTIFY_PLAYS_GROWTH	WK3_SPOTIFY_PLAYS_GROWTH	WK4_SPOTIFY_PLAYS_GROWTH	WK5_SPOTIFY_PLAYS_GROWTH	WK6_SPOTIFY_PLAYS_GROWTH	WK7_SPOTIFY_PLAYS_GROWTH	WK8_SPOTIFY_PLAYS_GROWTH	WK9_SPOTIFY_PLAYS_GROWTH	WK10_SPOTIFY_PLAYS_GROWTH	WK11_SPOTIFY_PLAYS_GROWTH	WK12_SPOTIFY_PLAYS_GROWTH	WK13_SPOTIFY_PLAYS_GROWTH	WK14_SPOTIFY_PLAYS_GROWTH	WK15_SPOTIFY_PLAYS_GROWTH	WK1_TIKTOK_POSTS_GROWTH	WK2_TIKTOK_POSTS_GROWTH	WK3_TIKTOK_POSTS_GROWTH	WK4_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
52	67519811	Lullaby	20930561	Lovesick	2022-04-29	Jay Isaiah	CA	Canada	462882.0	Pop	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9924.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
111	19087846	Addicted	2460010	Addicted	2018-04-18	VanJess	US	United States	462889.0	R&B/Soul	1	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	-2.0	17.0	0.0	0.0	0.0	0.0	0.0	-13.0	0.0	0.0	0.0	0.0	0.0	10.0	0.0	R&B/Soul	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	14.0	3.0	0.0	0.0	11.0	0.0	10.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-19.0	0.0	-256.0	0.0	-24.0	0.0	-55.0
305	13381429	Hey Now	6310404	MAN WITH A "BEST" MISSION (Deluxe Edition)	2020-07-15	MAN WITH A MISSION	JP	Japan	462924.0	J-Pop	1	3212839.0	380.0	0.0	23.0	0.0	4.0	163390.0	620.0	0.0	4.0	0.0	1.0	155665.0	709.0	0.0	-17.0	0.0	2.0	163889.0	567.0	0.0	3.0	0.0	2.0	J-Pop	0.0	42325.0	52655.0	52105.0	54874.0	56411.0	56727.0	44611.0	54327.0	58400.0	56525.0	48964.0	0.0	122.0	160.0	180.0	223.0	217.0	237.0	230.0	242.0	224.0	209.0	134.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	2.0	2.0	0.0	19.0	0.0	14.0	0.0	0.0	16.0	9.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	3.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	-309670367.0	0.0	0.0	0.0	0.0	1485514.0	0.0	0.0	1766148.0	0.0	-81464503.0	2887717.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
500	12265525	Selfie	3074223	Selfie	2015-07-06	Vald	FR	France	462883.0	Hip-Hop/Rap	1	3430.0	10.0	0.0	4.0	0.0	0.0	11615182.0	69715.0	0.0	48.0	0.0	0.0	51574.0	416.0	0.0	-51.0	0.0	0.0	-11666181.0	-70127.0	0.0	51.0	0.0	0.0	Hip-Hop/Rap	0.0	28944.0	0.0	11569596.0	0.0	11612730.0	16110.0	19008.0	16456.0	17967.0	19495.0	0.0	0.0	211.0	0.0	69322.0	0.0	69725.0	118.0	136.0	162.0	120.0	168.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	48.0	0.0	0.0	0.0	0.0	0.0	0.0	51.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	240293.0	11124691.0	0.0	0.0	0.0	0.0	317784.0	-20921733.0	0.0	0.0	11539083.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
509	21815691	Starlight	3365695	Starlight	2018-10-19	BABYMETAL	JP	Japan	462884.0	Rock	1	5179619.0	3264.0	0.0	22.0	47.0	4.0	156612.0	2625.0	0.0	24.0	20.0	0.0	153045.0	2502.0	0.0	-36.0	-62.0	5.0	129372.0	2046.0	0.0	16.0	73.0	4.0	Hard Rock	0.0	70830.0	75610.0	56308.0	56591.0	43713.0	51029.0	54787.0	47229.0	46026.0	42660.0	40686.0	0.0	1258.0	1409.0	909.0	978.0	738.0	901.0	868.0	733.0	751.0	654.0	641.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9.0	0.0	32.0	0.0	0.0	0.0	10.0	17.0	0.0	0.0	0.0	0.0	47.0	12.0	6.0	2.0	0.0	6.0	0.0	1.0	6.0	66.0	0.0	0.0	1.0	0.0	0.0	0.0	2.0	1.0	2.0	1.0	0.0	3.0	0.0	0.0	-308425708.0	0.0	-46136631.0	-5781012.0	0.0	-349708.0	0.0	0.0	1333673.0	0.0	-4050805.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-18748.0	0.0	0.0
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101731	36087839	Cringe	8353312	Cringe / You're the Boss	2021-04-30	Sophiegrophy	AU	Australia	462883.0	Hip-Hop/Rap	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	18181.0	-7.0	0.0	0.0	0.0	0.0	12058.0	-1.0	1.0	0.0	Electronic	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9413.0	6944.0	5799.0	5438.0	3823.0	4538.0	3697.0	0.0	0.0	0.0	0.0	34.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
101993	14488464	Intoxicated - Radio Edit	3100017	House Every Weekend, Vol. 2	2016-03-11	Good Times Ahead	US	United States	462887.0	Electronic	1	164890036.0	886774.0	0.0	16.0	0.0	1966.0	-164549695.0	-895961.0	0.0	1.0	0.0	1475.0	-320677.0	-3967.0	9021688.0	-2.0	0.0	1548.0	171312941.0	955695.0	7668998.0	2.0	0.0	1375.0	Electronic	0.0	935575.0	1015869.0	1045060.0	752381.0	0.0	167600702.0	664096.0	0.0	169914064.0	666934.0	731943.0	0.0	6699.0	8561.0	7011.0	5700.0	0.0	918933.0	5116.0	0.0	939578.0	7623.0	8494.0	0.0	0.0	0.0	0.0	0.0	3350202.0	3223171.0	3040072.0	2758445.0	2750294.0	2595633.0	2323071.0	0.0	0.0	10.0	0.0	1.0	1.0	0.0	0.0	58.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	621.0	672.0	711.0	377.0	387.0	495.0	514.0	539.0	433.0	436.0	506.0	0.0	0.0	147613139.0	161835676.0	0.0	158263776.0	0.0	0.0	0.0	0.0	123682267.0	65697172.0	0.0	0.0	161511553.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102072	12315279	Pose ton Gun	1798878	Pose Ton Gun	1999-09-13	Suprême NTM	FR	France	462883.0	Hip-Hop/Rap	1	20612933.0	67823.0	0.0	31.0	23.0	5.0	624635.0	3426.0	0.0	0.0	19.0	9.0	575967.0	3376.0	0.0	-22.0	0.0	3.0	-19606269.0	-68130.0	0.0	2.0	0.0	7.0	Hip-Hop/Rap	0.0	214443.0	214299.0	225924.0	223350.0	175361.0	176710.0	212980.0	186277.0	185571.0	171696.0	0.0	0.0	1173.0	1227.0	1192.0	1242.0	992.0	1002.0	1384.0	990.0	974.0	1204.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	115179.0	0.0	4.0	0.0	0.0	3.0	0.0	0.0	0.0	6.0	1.0	0.0	1.0	0.0	0.0	11.0	4.0	11.0	4.0	4.0	10.0	0.0	0.0	2.0	22.0	0.0	4.0	1.0	2.0	5.0	2.0	0.0	1.0	2.0	2.0	4.0	1.0	-674927955.0	-124377253.0	-531138.0	17032541.0	17441068.0	0.0	0.0	16608033.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-257.0	0.0	0.0	0.0	0.0	194.0	0.0
102128	59708880	All Night	11884894	HEY WHAT	2021-09-10	Low	US	United States	462888.0	Alternative	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	275325.0	0.0	0.0	39.0	Alternative	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	101052.0	55464.0	118809.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	22.0	5.0	12.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102389	13469059	Lullaby	12138962	Mountains - Massive Rock Hits	2021-07-02	Nickelback	CA	Canada	462882.0	Pop	1	116202530.0	8699.0	0.0	34.0	0.0	0.0	1373131.0	7558.0	0.0	-1.0	0.0	0.0	1173909.0	7138.0	0.0	-35.0	0.0	0.0	1077496.0	6812.0	0.0	1.0	0.0	0.0	Metal	0.0	512034.0	574319.0	527586.0	483271.0	362274.0	340953.0	445594.0	387362.0	420203.0	334885.0	322408.0	0.0	2718.0	3095.0	2895.0	2617.0	2046.0	2007.0	2706.0	2425.0	2695.0	2014.0	2103.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	97897749.0	0.0	0.0	0.0	88628371.0	0.0	0.0	0.0	0.0	109205678.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_PLAYS_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_PLAYS_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M1_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M1_YOUTUBE_LIKES_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M1_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_PLAYS_GROWTH	M3_SPOTIFY_PLAYS_GROWTH	M4_SPOTIFY_PLAYS_GROWTH	M5_SPOTIFY_PLAYS_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M1_SPOTIFY_POPULARITY_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M1_TIKTOK_POSTS_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M1_AIRPLAY_STREAMS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK1_SPOTIFY_PLAYS_GROWTH	WK2_SPOTIFY_PLAYS_GROWTH	WK3_SPOTIFY_PLAYS_GROWTH	WK4_SPOTIFY_PLAYS_GROWTH	WK5_SPOTIFY_PLAYS_GROWTH	WK6_SPOTIFY_PLAYS_GROWTH	WK7_SPOTIFY_PLAYS_GROWTH	WK8_SPOTIFY_PLAYS_GROWTH	WK9_SPOTIFY_PLAYS_GROWTH	WK10_SPOTIFY_PLAYS_GROWTH	WK11_SPOTIFY_PLAYS_GROWTH	WK12_SPOTIFY_PLAYS_GROWTH	WK13_SPOTIFY_PLAYS_GROWTH	WK14_SPOTIFY_PLAYS_GROWTH	WK15_SPOTIFY_PLAYS_GROWTH	WK1_TIKTOK_POSTS_GROWTH	WK2_TIKTOK_POSTS_GROWTH	WK3_TIKTOK_POSTS_GROWTH	WK4_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
5	15905276	Everybody Knows	801953	Everybody Knows EP (International Version)	2007-01-01	Ryan Adams	US	United States	462891.0	Singer/Songwriter	1	2398687.0	93.0	0.0	32.0	0.0	20.0	11925.0	63.0	0.0	0.0	0.0	11.0	10860.0	54.0	0.0	-13.0	0.0	20.0	10756.0	62.0	0.0	2.0	0.0	23.0	Alternative	0.0	5786.0	5152.0	3484.0	4269.0	4172.0	3925.0	4275.0	2660.0	3453.0	3743.0	3560.0	0.0	40.0	45.0	19.0	22.0	22.0	24.0	17.0	13.0	20.0	20.0	22.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	14.0	0.0	13.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	4.0	5.0	5.0	1.0	9.0	5.0	6.0	10.0	8.0	5.0	0.0	0.0	-6208611.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-1366261.0	0.0	0.0	2336211.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
27	15966354	Flex	147413	Flex (feat. Tory Lanez & Fabolous) - Single	2016-07-01	Joe Budden	US	United States	462883.0	Hip-Hop/Rap	1	1260510.0	88.0	0.0	2.0	0.0	0.0	7031.0	76.0	0.0	-1.0	0.0	0.0	6931.0	75.0	0.0	-3.0	0.0	0.0	5891.0	68.0	0.0	2.0	0.0	0.0	Hip-Hop/Rap	0.0	1848.0	2577.0	2534.0	2138.0	2359.0	2288.0	2168.0	2475.0	1527.0	2125.0	2239.0	0.0	38.0	25.0	23.0	27.0	26.0	22.0	25.0	28.0	21.0	22.0	25.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	1.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-9994086.0	0.0	-35213594.0	0.0	0.0	0.0	-7608523.0	-5207633.0	-5500156.0	949964.0	-344181195.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
52	67519811	Lullaby	20930561	Lovesick	2022-04-29	Jay Isaiah	CA	Canada	462882.0	Pop	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9924.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
62	13123423	Red	204739	Bloodworks	2013-04-29	Phoria	GB	United Kingdom	462894.0	New Age	1	3244.0	437.0	0.0	37.0	0.0	2.0	51557.0	-201.0	0.0	-2.0	2.0	2.0	118495.0	1711.0	0.0	-16.0	0.0	2.0	-167598.0	-1494.0	0.0	-12.0	0.0	1.0	Electronic	0.0	0.0	0.0	37458.0	115480.0	0.0	106140.0	0.0	117265.0	0.0	37782.0	0.0	0.0	0.0	0.0	0.0	1838.0	0.0	1636.0	0.0	1708.0	0.0	0.0	450.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	26261.0	0.0	0.0	17.0	0.0	30.0	0.0	0.0	0.0	18.0	2.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	113207.0	-979203.0	0.0	0.0	0.0	-73192747.0	-129128.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-10.0	0.0	0.0	0.0	0.0	-1316.0	-335.0
73	28604194	Happy Days	6940264	Something to Say	2020-10-30	Cory Henry	US	United States	462933.0	Funk	1	0.0	0.0	0.0	13.0	2.0	12.0	0.0	0.0	0.0	-1.0	4.0	11.0	0.0	0.0	373025.0	0.0	5.0	6.0	0.0	0.0	415501.0	0.0	6.0	25.0	Funk	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	96013.0	102781.0	130579.0	139665.0	161427.0	120146.0	133928.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	2.0	3.0	0.0	0.0	6.0	1.0	0.0	0.0	3.0	4.0	4.0	4.0	3.0	1.0	2.0	3.0	5.0	10.0	10.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	-515.0	-855.0	0.0
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102483	15188851	Que Me Vas a Dar	6876025	Parrandera, Rebelde y Atrevida (Deluxe)	2011-05-03	Jenni Rivera	US	United States	462951.0	Latin	1	42702260.0	4368.0	0.0	13.0	568.0	7.0	573944.0	3551.0	0.0	0.0	239.0	8.0	469755.0	3225.0	0.0	-2.0	604.0	11.0	453843.0	3086.0	0.0	3.0	-1037.0	8.0	Latin	0.0	219455.0	222369.0	217752.0	188172.0	168020.0	174197.0	142153.0	153405.0	145325.0	125515.0	183003.0	0.0	1398.0	1403.0	1259.0	1132.0	1160.0	1083.0	1024.0	1118.0	1037.0	808.0	1241.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	820891.0	0.0	9.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	2.0	0.0	29.0	323.0	138.0	51.0	50.0	0.0	1466.0	105.0	232.0	172.0	0.0	0.0	1.0	3.0	3.0	3.0	2.0	3.0	5.0	3.0	2.0	5.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	18986634.0	41853721.0	36746157.0	0.0	-48794961.0	0.0	0.0	0.0	42894393.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-13321.0	0.0	280.0	0.0	-209.0	366.0	0.0	0.0	401.0	0.0	712.0	0.0	0.0
102490	13277835	In equilibrio	7478192	Eternamente Ora	2016-02-12	Francesco Gabbani	IT	Italy	462920.0	Soft Rock	1	1484012.0	128.0	0.0	26.0	0.0	0.0	17852.0	125.0	0.0	-8.0	0.0	0.0	12826.0	71.0	0.0	0.0	0.0	0.0	16955.0	134.0	0.0	-4.0	0.0	0.0	Pop	0.0	7148.0	7884.0	6633.0	6223.0	4996.0	3361.0	4394.0	5071.0	5031.0	6351.0	5573.0	0.0	40.0	58.0	46.0	42.0	37.0	18.0	24.0	29.0	32.0	49.0	53.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	3.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-229535.0	0.0	0.0	0.0	-3587990.0	-4416954.0	0.0	0.0	0.0	-60567291.0	-45723177.0	-2374403.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102512	31721983	Underdog	6898771	Underdog	2020-10-19	Ummet Ozcan	NL	Netherlands	462890.0	House	1	0.0	0.0	0.0	0.0	0.0	21.0	0.0	0.0	0.0	-3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	Electronic	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102517	35961220	Lost In Love	8347744	Lost In Love	2021-04-28	Jany Green	US	United States	462889.0	R&B/Soul	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	107953.0	-16.0	-1.0	0.0	0.0	0.0	123607.0	0.0	2.0	0.0	Pop	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	39139.0	25521.0	50125.0	32307.0	37074.0	44978.0	41555.0	0.0	0.0	0.0	0.0	46.0	0.0	0.0	0.0	7.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102532	17882234	There Are Many Ways To Say I Love You	5721641	Musique Love	2020-02-21	Sylvan Esso	US	United States	462884.0	Rock	1	5704.0	0.0	0.0	43.0	0.0	1.0	192772.0	2458.0	0.0	1.0	0.0	0.0	2575.0	39.0	0.0	-44.0	0.0	1.0	-195347.0	-2497.0	0.0	0.0	0.0	4.0	Alternative	0.0	1106.0	0.0	191049.0	0.0	192772.0	817.0	0.0	195347.0	0.0	196996.0	0.0	0.0	19.0	0.0	2433.0	0.0	2458.0	7.0	0.0	2497.0	0.0	2524.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	2.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	-11949793.0	0.0	0.0	0.0	-146655176.0	0.0	-158090438.0	0.0	-52014.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK1_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
4414	14794732	Boom and Bend Over	1529681	Boom and Bend Over - Single	2014-08-12	J Capri	AU	Australia	462882.0	Pop	1	7533526.0	34821.0	24.0	0.0	0.0	14738.0	213.0	1.0	0.0	0.0	-6744010.0	-34892.0	0.0	-1.0	0.0	0.0	5635.0	63.0	0.0	3.0	-1.0	0.0	Dancehall	3455.0	3270.0	0.0	6745875.0	5041.0	5075.0	5629.0	0.0	6767342.0	6798.0	0.0	37.0	17.0	0.0	34893.0	76.0	66.0	91.0	0.0	35237.0	122.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	2.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	17.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-5844278.0	0.0	5205386.0	0.0	-217547084.0	7525707.0	0.0	-25413594.0	7535532.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
10384	15279844	You and I	354824	White Noise (Deluxe Version)	2016-04-22	PVRIS	US	United States	462882.0	Pop	1	7067833.0	102232.0	3.0	0.0	6.0	99661.0	1197.0	15.0	0.0	7.0	-5643211.0	-78407.0	1427200.0	-1.0	0.0	15.0	-718512.0	-7501.0	1428553.0	2.0	0.0	2.0	Alternative	0.0	7048241.0	0.0	5187.0	5662301.0	0.0	7239774.0	0.0	4697.0	0.0	780227.0	0.0	101856.0	0.0	61.0	78645.0	0.0	104986.0	0.0	68.0	0.0	17007.0	422156.0	450528.0	476205.0	500467.0	476951.0	501608.0	449994.0	0.0	1.0	0.0	16.0	0.0	0.0	1.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	13.0	21.0	3.0	2.0	1.0	0.0	6.0	14.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	-6891072.0	0.0	0.0	0.0	0.0	0.0	753318.0	0.0	0.0	0.0	6340873.0	0.0	0.0	8549.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
10583	11113521	Ainda Lembro	1323873	Silva Canta Marisa	2017-05-05	Silva	BR	Brazil	462883.0	Hip-Hop/Rap	1	8342109.0	53278.0	51.0	0.0	1.0	482297.0	3189.0	-1.0	0.0	1.0	-7975094.0	-56110.0	0.0	-30.0	1.0	2.0	11437.0	105.0	0.0	32.0	0.0	3.0	Hip-Hop/Rap	106591.0	152209.0	0.0	7854106.0	143651.0	165039.0	191836.0	0.0	8666540.0	171380.0	0.0	696.0	1037.0	0.0	55246.0	1049.0	1136.0	1071.0	0.0	60291.0	881.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	30.0	2.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	1.0	0.0	2.0	0.0	1.0	0.0	-14082688.0	0.0	0.0	7640883.0	0.0	7331141.0	0.0	0.0	-934272.0	8253498.0	0.0	-2830006.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
17014	13296371	You and I	1162693	La dolce vita (50 Chillout)	2014-02-17	Delegation	GB	United Kingdom	462889.0	R&B/Soul	1	7921.0	0.0	0.0	11.0	60.0	166.0	1.0	53.0	18.0	58.0	14219.0	241.0	853253.0	-27.0	-82.0	0.0	-13784.0	-234.0	694556.0	27.0	0.0	0.0	Funk	0.0	0.0	20575.0	0.0	37.0	92.0	31.0	14096.0	0.0	20997.0	0.0	0.0	0.0	339.0	0.0	0.0	0.0	1.0	240.0	0.0	339.0	0.0	270038.0	283712.0	295081.0	274460.0	233677.0	201554.0	259325.0	0.0	0.0	1.0	52.0	0.0	0.0	0.0	0.0	27.0	0.0	1.0	0.0	8.0	9.0	7.0	2.0	0.0	86.0	0.0	0.0	0.0	0.0	17.0	18.0	16.0	21.0	21.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-21617397.0	0.0	-5126512.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-7469.0	0.0	0.0	-138.0	0.0	0.0
18232	13348974	Lost In The Weekend	1815545	Più Che Logico (Live)	2015-05-26	Cesare Cremonini	IT	Italy	462882.0	Pop	1	5938114.0	29354.0	44.0	0.0	48.0	74458.0	476.0	1.0	0.0	43.0	-5997715.0	-29753.0	0.0	-27.0	0.0	0.0	160441.0	839.0	0.0	2.0	0.0	0.0	Pop	24185.0	26537.0	25679.0	25162.0	23617.0	22156.0	25697.0	0.0	146309.0	7619.0	6513.0	147.0	155.0	153.0	170.0	153.0	139.0	142.0	0.0	772.0	37.0	30.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	26.0	0.0	1.0	1.0	0.0	0.0	0.0	2.0	1.0	0.0	0.0	0.0	0.0	6.0	0.0	2.0	2.0	0.0	0.0	0.0	0.0	13.0	15.0	16.0	18.0	9.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-1675509.0	0.0	0.0	-17395708.0	0.0	0.0	0.0	0.0	0.0	0.0	-7080601.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
24077	13459953	Senza 'e te	1119458	Medina	2001-03-31	Pino Daniele	IT	Italy	462900.0	Blues	1	5865432.0	18394.0	10.0	200.0	12.0	70463.0	532.0	0.0	186.0	13.0	-5621354.0	-18714.0	0.0	-11.0	0.0	13.0	47233.0	217.0	0.0	3.0	0.0	10.0	Blues	35654.0	25414.0	26417.0	24437.0	19609.0	21837.0	0.0	14230.0	5723903.0	0.0	12796.0	231.0	164.0	201.0	198.0	133.0	178.0	0.0	78.0	19496.0	0.0	62.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	2.0	89.0	86.0	60.0	63.0	63.0	38.0	61.0	0.0	0.0	41.0	0.0	3.0	4.0	4.0	5.0	4.0	4.0	5.0	4.0	4.0	2.0	4.0	0.0	-41165484.0	3783265.0	0.0	0.0	5230626.0	0.0	-177080377.0	4922070.0	0.0	0.0	-885587.0	-4125339.0	0.0	4773367.0	0.0	208.0	517.0	409.0	543.0	0.0	625.0	233.0	0.0	619.0	0.0	703.0
25079	12983603	L.E.S Artistes	1255753	Santogold	2008-05-12	Santigold	US	United States	462888.0	Alternative	1	6586272.0	30833.0	41.0	0.0	0.0	191713.0	1929.0	-1.0	0.0	0.0	-6327914.0	-29439.0	0.0	-40.0	0.0	8.0	74577.0	1016.0	0.0	41.0	0.0	0.0	Alternative	65060.0	76518.0	62542.0	71858.0	57313.0	61663.0	0.0	24862.0	25584.0	25318.0	23675.0	646.0	737.0	530.0	801.0	598.0	669.0	0.0	364.0	384.0	333.0	299.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	40.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	8.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-43604265.0	0.0	5921978.0	0.0	4375375.0	0.0	2220041.0	5778092.0	6569194.0	6598573.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
27624	13250157	Pensiero stupendo	2045684	Aristocratica	1998-11-04	Patty Pravo	IT	Italy	462882.0	Pop	1	9210669.0	24685.0	8.0	64.0	68.0	143560.0	861.0	-6.0	134.0	76.0	-5548820.0	-11241.0	856858.0	-34.0	128.0	81.0	392954.0	1453.0	765335.0	40.0	102.0	69.0	Pop	50138.0	54314.0	0.0	9282892.0	44602.0	0.0	9453636.0	0.0	5743419.0	48542.0	0.0	309.0	331.0	0.0	25164.0	276.0	0.0	26160.0	0.0	12357.0	265.0	0.0	289318.0	283082.0	308019.0	265757.0	264403.0	241695.0	259237.0	0.0	8.0	0.0	0.0	0.0	0.0	0.0	0.0	40.0	0.0	0.0	35.0	27.0	41.0	59.0	34.0	0.0	0.0	666.0	0.0	839.0	30.0	24.0	23.0	22.0	27.0	27.0	23.0	26.0	32.0	23.0	20.0	26.0	0.0	0.0	-66649373.0	-31079235.0	0.0	0.0	-237527590.0	0.0	0.0	0.0	8990083.0	0.0	0.0	-58941.0	0.0	0.0	0.0	0.0	-9641.0	-62830.0	0.0	454.0	0.0	349.0	0.0	507.0	502.0
27873	11101567	Mesmo Que Seja Eu	5769961	Anos 80 As Melhores	2020-03-06	Marina Lima	BR	Brazil	462885.0	Dance	1	0.0	0.0	12.0	0.0	1.0	0.0	0.0	38.0	1.0	2.0	0.0	0.0	475532.0	-50.0	0.0	13.0	0.0	0.0	507035.0	51.0	0.0	13.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	156945.0	152187.0	156606.0	166739.0	163883.0	156986.0	186166.0	0.0	0.0	37.0	0.0	1.0	0.0	0.0	0.0	8.0	42.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	2.0	4.0	7.0	4.0	5.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-26199.0	0.0	0.0	0.0	-7801.0	-10.0
34538	16552699	Great Escape	4806175	Best Of The Super Cinema 2008-2011/2012-2019	2019-09-18	cinema staff	JP	Japan	462884.0	Rock	1	6696797.0	71829.0	17.0	0.0	21.0	322103.0	4005.0	-1.0	0.0	19.0	-6711350.0	-75721.0	1499671.0	-48.0	0.0	9.0	3616.0	52.0	1430722.0	48.0	0.0	4.0	Alternative	230900.0	181993.0	123672.0	108136.0	90295.0	87265.0	86146.0	0.0	7037449.0	0.0	1082.0	3238.0	2199.0	1465.0	1392.0	1148.0	930.0	1058.0	0.0	79639.0	0.0	12.0	542522.0	512379.0	518408.0	468884.0	490613.0	440305.0	499804.0	0.0	2.0	0.0	0.0	0.0	0.0	43.0	0.0	47.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	11.0	4.0	6.0	11.0	2.0	2.0	4.0	3.0	1.0	3.0	0.0	0.0	0.0	5103444.0	0.0	0.0	0.0	0.0	-35577021.0	0.0	3312741.0	6091444.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
37807	12366526	New Age	136334	538 Hitzone 61	2012-01-01	Marlon Roudette	GB	United Kingdom	462882.0	Pop	1	8354058.0	22013.0	53.0	0.0	1781.0	8144.0	91.0	-26.0	0.0	1712.0	-7436278.0	-9048.0	0.0	31.0	0.0	1687.0	-922090.0	-13040.0	0.0	0.0	0.0	1532.0	Dance	2246.0	3124.0	0.0	480.0	7438996.0	2268.0	2539.0	0.0	401.0	7446578.0	0.0	23.0	49.0	0.0	15.0	9146.0	30.0	33.0	0.0	11.0	9184.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	26.0	0.0	0.0	53.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	575.0	596.0	565.0	551.0	596.0	583.0	565.0	539.0	506.0	540.0	486.0	-6267626.0	0.0	7590218.0	0.0	7018167.0	0.0	7915149.0	0.0	0.0	0.0	822803.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
43844	17198795	higher	5181141	higher	2014-03-05	MAN WITH A MISSION	JP	Japan	462924.0	J-Pop	1	6213202.0	13232.0	1.0	0.0	2.0	85385.0	276.0	25.0	0.0	2.0	-5999854.0	-13155.0	0.0	-18.0	0.0	2.0	30709.0	207.0	0.0	-4.0	0.0	6.0	J-Pop	34238.0	33152.0	26289.0	31729.0	27367.0	31195.0	0.0	6250.0	8011.0	10732.0	11966.0	90.0	206.0	74.0	103.0	99.0	120.0	0.0	41.0	52.0	74.0	81.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	24.0	0.0	29.0	0.0	20.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	1.0	1.0	0.0	2.0	4.0	0.0	0.0	-33600143.0	5348575.0	443460.0	0.0	-21993830.0	0.0	-3446453.0	2563709.0	0.0	0.0	-198620711.0	-2854356.0	0.0	4871963.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
48753	16030439	Kiss Somebody	5154971	Country Heat 2019	2018-11-09	Morgan Evans	US	United States	462895.0	Country	1	6495812.0	49849.0	50.0	2.0	1028.0	68597.0	630.0	-10.0	3.0	733.0	-6548343.0	-50267.0	4056718.0	7.0	-6.0	647.0	71986.0	733.0	3832215.0	1.0	1.0	811.0	Country	23577.0	24319.0	21369.0	24500.0	22728.0	25398.0	22401.0	0.0	6636771.0	17864.0	0.0	177.0	213.0	167.0	234.0	229.0	266.0	229.0	0.0	51133.0	155.0	0.0	1540341.0	1493847.0	1358985.0	1203886.0	1479576.0	1144887.0	1207752.0	8.0	3.0	0.0	0.0	0.0	8.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	3.0	0.0	2.0	0.0	0.0	324.0	290.0	250.0	242.0	241.0	213.0	195.0	239.0	222.0	256.0	333.0	0.0	5432866.0	0.0	0.0	0.0	-4036214.0	2290655.0	0.0	0.0	4837158.0	0.0	-19144258.0	3661698.0	-124729402.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-1230.0	-110.0	54.0	-22115.0	0.0
49593	15910471	Amazin'	6409711	Go Crazy	2020-07-28	Jeezy	US	United States	462883.0	Hip-Hop/Rap	1	5089649.0	55629.0	49.0	0.0	1.0	916756.0	7503.0	-1.0	0.0	0.0	-5683674.0	-59033.0	0.0	-48.0	0.0	0.0	-223398.0	-4099.0	0.0	48.0	0.0	0.0	Hip-Hop/Rap	303623.0	364904.0	304524.0	0.0	5683674.0	0.0	6408982.0	0.0	7104081.0	0.0	0.0	2946.0	3100.0	2326.0	0.0	59033.0	0.0	65059.0	0.0	70916.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	36.0	0.0	0.0	0.0	0.0	0.0	0.0	14.0	0.0	34.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-2055348.0	0.0	2600945.0	-3245319.0	0.0	-18174300.0	0.0	718445.0	0.0	4913205.0	3989490.0	313214.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
58288	15829591	You and I	1889104	I Am	1979-06-01	Earth, Wind & Fire	US	United States	462889.0	R&B/Soul	1	0.0	0.0	40.0	22.0	1.0	0.0	0.0	-15.0	7.0	3.0	0.0	0.0	0.0	-1.0	-118.0	7.0	0.0	0.0	0.0	1.0	116.0	2.0	R&B/Soul	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	25.0	13.0	1.0	2.0	4.0	1.0	3.0	0.0	0.0	0.0	114.0	2.0	0.0	1.0	2.0	1.0	0.0	5.0	0.0	2.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-357.0	0.0	0.0	103.0	-25688.0	0.0	0.0	0.0	0.0	-286.0	-1278.0	109.0
60643	18072028	You and I	5145535	Best of 2018	2018-12-07	ALPHA 9	RU	Russian Federation	462927.0	Trance	1	0.0	0.0	6.0	0.0	0.0	0.0	0.0	27.0	0.0	0.0	0.0	0.0	0.0	-12.0	2.0	0.0	0.0	0.0	0.0	-21.0	1.0	0.0	Electronic	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	27.0	1.0	0.0	0.0	23.0	0.0	0.0	23.0	0.0	0.0	0.0	0.0	0.0	2.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
62445	61549041	You and I	15073143	This Time I See It	2021-09-24	The Franklin Electric	CA	Canada	462884.0	Rock	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	Alternative	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	32165.0	26392.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
68417	11102884	Mesmo Que Seja Eu	10986506	Erasmo 80	2021-06-03	Erasmo Carlos	BR	Brazil	462882.0	Pop	1	6597585.0	40600.0	14.0	9.0	8.0	120532.0	1300.0	1.0	2.0	4.0	-5488327.0	-36436.0	0.0	-20.0	-2.0	6.0	779476.0	17760.0	0.0	2.0	46.0	5.0	Pop	5354525.0	51720.0	0.0	5468357.0	42480.0	46819.0	50196.0	0.0	5673981.0	0.0	133398.0	35309.0	469.0	0.0	36233.0	533.0	406.0	441.0	0.0	38212.0	0.0	1461.0	0.0	0.0	0.0	0.0	0.0	0.0	117505.0	0.0	2.0	0.0	1.0	2.0	0.0	0.0	1.0	1.0	0.0	2.0	0.0	6.0	0.0	0.0	2.0	0.0	14.0	0.0	26.0	7.0	13.0	3.0	0.0	1.0	3.0	0.0	4.0	1.0	1.0	2.0	0.0	3.0	0.0	0.0	0.0	-1531917.0	6191846.0	0.0	0.0	0.0	0.0	0.0	6234417.0	0.0	-4070324.0	6111699.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	19.0	-1740.0
68497	13377852	名前のない怪物	1026864	名前のない怪物	2012-11-27	EGOIST	JP	Japan	462885.0	Dance	1	11722032.0	74834.0	10.0	0.0	2.0	228082.0	2667.0	3.0	0.0	7.0	-7068277.0	-75677.0	1161895.0	-2.0	78.0	5.0	61633.0	586.0	1278571.0	5.0	32.0	7.0	Dance	76691.0	96933.0	77255.0	0.0	7160910.0	0.0	22551.0	27607.0	25656.0	15919.0	20058.0	890.0	1011.0	814.0	0.0	76520.0	0.0	192.0	269.0	260.0	141.0	185.0	386501.0	383646.0	396948.0	381301.0	441537.0	410849.0	426185.0	0.0	5.0	1.0	1.0	1.0	0.0	0.0	0.0	3.0	0.0	2.0	0.0	0.0	0.0	2.0	0.0	1.0	0.0	0.0	7.0	10.0	15.0	1.0	0.0	0.0	1.0	6.0	4.0	0.0	1.0	2.0	4.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	-6264526.0	0.0	10212993.0	6264029.0	-49230364.0	0.0	0.0	0.0	11456732.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
69016	18581755	Colors of the Heart	2715599	ALL TIME BEST -FAN BEST-	2018-07-18	UVERworld	JP	Japan	462883.0	Hip-Hop/Rap	1	6643673.0	86349.0	7.0	20.0	20.0	235779.0	2744.0	-2.0	-1.0	17.0	-6594068.0	-89053.0	0.0	-26.0	-175.0	10.0	1069.0	19.0	0.0	3.0	10.0	7.0	Hip-Hop/Rap	106596.0	97588.0	77986.0	83681.0	74112.0	81261.0	87413.0	0.0	6938707.0	0.0	349.0	1165.0	1015.0	886.0	1076.0	782.0	877.0	1062.0	0.0	92919.0	0.0	11.0	0.0	0.0	0.0	0.0	0.0	0.0	263350.0	0.0	2.0	0.0	0.0	1.0	0.0	0.0	22.0	2.0	0.0	9.0	8.0	11.0	3.0	0.0	4.0	12.0	15.0	0.0	0.0	0.0	0.0	6.0	7.0	7.0	8.0	2.0	2.0	4.0	4.0	1.0	3.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5576000.0	1460731.0	4423029.0	0.0	0.0	-41590706.0	0.0	0.0	0.0	0.0	0.0	-10934.0	0.0	53.0	0.0	57.0	0.0	-11721.0	0.0
70568	41575962	You and I	9615450	You and I	2021-05-14	Paola Navarrete	EC	Ecuador	462889.0	R&B/Soul	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4476.0	-12.0	0.0	1.0	0.0	0.0	3144.0	-1.0	0.0	0.0	Hip-Hop/Rap	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5585.0	1627.0	1527.0	1322.0	807.0	1426.0	911.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
75093	15729592	Qué Manera de Quererte	3912668	Perdoname...¡Exitos!	2000-01-01	Gilberto Santa Rosa	PR	Puerto Rico	462904.0	Salsa	1	5279638.0	11602.0	16.0	1.0	85.0	1393051.0	2649.0	-1.0	31.0	88.0	-6175142.0	-13034.0	0.0	-20.0	-67.0	86.0	76719.0	368.0	0.0	7.0	116.0	48.0	Holiday	331906.0	394443.0	394340.0	490118.0	508593.0	0.0	27696.0	26466.0	28072.0	8315549.0	0.0	588.0	864.0	767.0	947.0	935.0	0.0	137.0	141.0	140.0	17256.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	6.0	0.0	0.0	5.0	17.0	9.0	0.0	78.0	0.0	5.0	102.0	9.0	28.0	33.0	29.0	29.0	30.0	34.0	27.0	25.0	21.0	18.0	9.0	0.0	-4782126.0	0.0	-2947599.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5079376.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	31.0	0.0	-2243.0	12.0	0.0	0.0
84241	13246762	Senza 'e te	770019	Fiorella	2014-11-20	Fiorella Mannoia	IT	Italy	462882.0	Pop	1	23923.0	3.0	2.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	0.0	-2.0	0.0	0.0	3465.0	27.0	0.0	2.0	0.0	0.0	Pop	0.0	0.0	0.0	0.0	0.0	0.0	693.0	1806.0	883.0	1796.0	786.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	16.0	7.0	10.0	10.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-43098215.0	0.0	0.0	0.0	-4793903.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
87988	15555431	MMMBop	5835080	Songs To Wash Your Hands To	2020-03-27	Hanson	US	United States	462882.0	Pop	1	6127773.0	48108.0	43.0	304.0	2683.0	41129.0	345.0	26.0	442.0	2529.0	-5883341.0	-44158.0	7485436.0	-1.0	-6311.0	499.0	-281804.0	-4293.0	132837844.0	3.0	6844.0	476.0	Pop	0.0	6107878.0	0.0	1380.0	5270818.0	14920.0	17449.0	0.0	5941341.0	0.0	0.0	0.0	47874.0	0.0	2.0	45188.0	119.0	188.0	0.0	44705.0	0.0	0.0	2351826.0	2591363.0	2509306.0	2384767.0	2725217.0	126946865.0	3165762.0	0.0	43.0	0.0	26.0	1.0	0.0	0.0	0.0	2.0	0.0	1.0	0.0	216.0	175.0	141.0	126.0	95.0	0.0	5.0	0.0	6836.0	14.0	806.0	906.0	910.0	944.0	675.0	93.0	207.0	199.0	209.0	132.0	135.0	0.0	0.0	-127339411.0	0.0	0.0	0.0	0.0	0.0	0.0	6024830.0	0.0	0.0	6092398.0	0.0	6125870.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5338.0	0.0	3903.0
88385	14507008	You and I	1056433	In My Room	2016-07-01	Jacob Collier	GB	United Kingdom	462882.0	Pop	1	645090.0	10740.0	3.0	0.0	1.0	20209.0	368.0	-2.0	0.0	3.0	-630278.0	-11110.0	0.0	0.0	0.0	10.0	672753.0	11997.0	0.0	0.0	0.0	2.0	Jazz	0.0	610069.0	0.0	623797.0	6481.0	0.0	644378.0	0.0	658272.0	0.0	672753.0	0.0	10740.0	0.0	10979.0	130.0	0.0	11436.0	0.0	11727.0	0.0	11997.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	2.0	1.0	2.0	5.0	3.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	-51632248.0	0.0	0.0	0.0	-161245653.0	0.0	0.0	606688.0	-16812158.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
92962	15117634	Money, Power & Respect (feat. DMX & Lil' Kim)	5559176	Hip Hop Klassiekers	2020-01-03	DMX	US	United States	462883.0	Hip-Hop/Rap	1	5836359.0	75510.0	56.0	10.0	139.0	1534214.0	20374.0	2.0	143.0	360.0	-7075657.0	-94912.0	1935343.0	0.0	-116.0	21.0	3893.0	45.0	1616468.0	1.0	67.0	27.0	Hip-Hop/Rap	204350.0	246818.0	855315.0	375498.0	303401.0	267491.0	0.0	1510.0	8405324.0	258663.0	0.0	2994.0	3166.0	13789.0	3467.0	3118.0	3042.0	0.0	23.0	110110.0	2723.0	0.0	518789.0	553172.0	763828.0	618343.0	606435.0	477898.0	532135.0	1.0	3.0	6.0	0.0	58.0	0.0	0.0	58.0	1.0	0.0	1.0	0.0	10.0	139.0	1.0	3.0	0.0	62.0	0.0	25.0	242.0	0.0	38.0	52.0	268.0	51.0	41.0	10.0	3.0	8.0	9.0	17.0	1.0	0.0	0.0	2913909.0	0.0	0.0	0.0	0.0	-137835.0	4975313.0	0.0	-28278.0	0.0	-782268.0	0.0	0.0	0.0	0.0	0.0	0.0	-91.0	-1.0	0.0	0.0	0.0	-89.0	23.0	-23.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK2_TIKTOK_POSTS_GROWTH	WK3_TIKTOK_POSTS_GROWTH	WK4_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
11168	12126263	Raelsan	96312	Le chant des sirènes	2011-09-26	Orelsan	FR	France	462883.0	Hip-Hop/Rap	1	10747972.0	56445.0	43.0	0.0	3.0	138389.0	1283.0	-1.0	0.0	2.0	133774.0	1325.0	0.0	-40.0	3.0	1.0	-10238460.0	-47704.0	0.0	5.0	8.0	98.0	Hip-Hop/Rap	47377.0	53620.0	49471.0	0.0	10141958.0	45100.0	44021.0	44653.0	0.0	10729038.0	0.0	355.0	458.0	452.0	0.0	44818.0	388.0	414.0	523.0	0.0	51402.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	43.0	0.0	0.0	0.0	1.0	0.0	2.0	1.0	2.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	1.0	45.0	35.0	18.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-142771.0	-2499727.0	0.0	0.0	0.0	0.0	10718853.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
14364	13277275	Bolla Papale Freestyle	1424676	MM Vol. 2	2016-11-11	MadMan	US	United States	462883.0	Hip-Hop/Rap	1	9620109.0	128656.0	1.0	0.0	0.0	72302.0	870.0	-1.0	0.0	0.0	77561.0	903.0	0.0	0.0	0.0	0.0	-9767146.0	0.0	0.0	0.0	0.0	0.0	Hip-Hop/Rap	24358.0	25988.0	0.0	0.0	9689603.0	25197.0	28846.0	23518.0	25605.0	35939.0	0.0	305.0	306.0	0.0	0.0	0.0	309.0	355.0	239.0	311.0	621.0	0.0	0.0	0.0	0.0	0.0	270605.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-6005567.0	0.0	0.0	0.0	0.0	4612259.0	0.0	-61102770.0	-4694131.0	8944102.0	0.0	0.0	8036864.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
15865	23101292	Picky Picky	14468308	LOCK END LOL	2019-05-14	Weki Meki	KR	South Korea	462925.0	K-Pop	1	11027508.0	342287.0	2.0	69.0	20.0	457296.0	9677.0	-1.0	115.0	20.0	242619.0	5580.0	824121.0	-9.0	-447.0	32.0	-10980520.0	-356487.0	759931.0	0.0	1.0	22.0	K-Pop	151477.0	190930.0	141873.0	176627.0	138796.0	105107.0	70583.0	66929.0	51778.0	69728.0	0.0	3181.0	4157.0	3115.0	4065.0	2497.0	2083.0	1756.0	1741.0	1261.0	2048.0	0.0	279609.0	280862.0	276591.0	266668.0	250596.0	235993.0	273342.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	43.0	39.0	45.0	31.0	24.0	18.0	0.0	42.0	46.0	0.0	8.0	6.0	4.0	9.0	7.0	14.0	12.0	6.0	8.0	6.0	8.0	0.0	0.0	0.0	0.0	0.0	0.0	8304470.0	0.0	0.0	0.0	9522444.0	0.0	10946925.0	10789413.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	272.0	0.0	0.0	0.0
17628	15090581	Left Right Left	3959753	Nine Track Mind Deluxe	2016-11-11	Charlie Puth	US	United States	462882.0	Pop	1	8439283.0	48437.0	2.0	899.0	11.0	142041.0	1057.0	0.0	-1.0	12.0	128090.0	964.0	0.0	-16.0	0.0	6.0	-8704585.0	-50451.0	402796.0	2.0	0.0	12.0	Pop	44792.0	54969.0	46586.0	0.0	8576689.0	45345.0	43205.0	39540.0	49789.0	41972.0	0.0	337.0	383.0	298.0	0.0	49490.0	336.0	309.0	319.0	389.0	273.0	0.0	0.0	0.0	0.0	79370.0	167236.0	119536.0	116024.0	0.0	2.0	0.0	7.0	0.0	0.0	0.0	10.0	0.0	0.0	898.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	860.0	0.0	3.0	6.0	4.0	5.0	3.0	1.0	1.0	4.0	5.0	5.0	2.0	0.0	0.0	5249591.0	7954391.0	0.0	0.0	0.0	-484053654.0	7894895.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-18297.0	0.0	0.0	0.0	0.0	0.0	0.0	-486.0
31016	11646564	You´re Not Alone	135506	Seven Years	2005-06-13	ATB	DE	Germany	462927.0	Trance	1	9157383.0	35266.0	5.0	0.0	171.0	163114.0	1354.0	-1.0	0.0	179.0	121154.0	1182.0	0.0	0.0	0.0	0.0	-9435154.0	-37782.0	0.0	0.0	8.0	85.0	Dance	64319.0	73603.0	61121.0	0.0	9315263.0	44860.0	40893.0	35401.0	35537.0	39285.0	0.0	600.0	621.0	429.0	0.0	36615.0	411.0	390.0	381.0	373.0	389.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	9.0	55.0	47.0	54.0	76.0	49.0	0.0	0.0	0.0	0.0	2.0	83.0	0.0	0.0	0.0	0.0	2253022.0	2085667.0	0.0	0.0	0.0	9081142.0	0.0	196763.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
41331	11084402	A Viagem	3102376	Vidavida	1994-08-28	Roupa Nova	BR	Brazil	462877.0	Sertanejo	1	13349190.0	104266.0	32.0	1623.0	24.0	105767.0	1133.0	-18.0	-1434.0	26.0	628560.0	6970.0	0.0	-3.0	-130.0	26.0	-11938541.0	-87346.0	0.0	3.0	150.0	41.0	Pop	0.0	0.0	0.0	0.0	12067614.0	181423.0	0.0	14026930.0	258436.0	0.0	101310.0	0.0	0.0	0.0	0.0	88981.0	2021.0	0.0	112370.0	2610.0	0.0	1188.0	0.0	0.0	0.0	0.0	0.0	0.0	165519.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	25.0	154.0	1468.0	0.0	1729.0	0.0	0.0	44.0	39.0	0.0	0.0	243.0	5.0	9.0	10.0	7.0	9.0	3.0	10.0	13.0	17.0	15.0	9.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-2298842.0	-3456791.0	0.0	0.0	-1604565.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	134.0	1708.0
44563	12588374	Sing It Back - Boris Dlugosch Mix	8026903	My Kitchen Rules	2012-01-01	Boris Dlugosch	DE	Germany	462890.0	House	1	12150213.0	63290.0	65.0	54.0	2578.0	0.0	-60.0	0.0	168.0	2814.0	0.0	-29.0	4052861.0	-2.0	-539.0	284.0	-11562091.0	-54835.0	4099523.0	3.0	1019.0	165.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	12073850.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	62640.0	0.0	0.0	0.0	1416690.0	1435676.0	1346098.0	1271087.0	1321401.0	1272780.0	1505342.0	1.0	2.0	0.0	0.0	27.0	0.0	0.0	2.0	0.0	1.0	0.0	54.0	66.0	49.0	53.0	0.0	25.0	0.0	0.0	744.0	276.0	783.0	883.0	957.0	981.0	876.0	83.0	113.0	88.0	66.0	51.0	48.0	0.0	0.0	0.0	0.0	0.0	10993961.0	0.0	0.0	5731942.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-771.0	0.0	0.0	0.0	0.0	0.0	307.0	0.0	0.0	-42726.0	393.0	0.0
49950	15815593	John Deere Green	181823	The Essential Joe Diffie	2008-07-08	Joe Diffie	US	United States	462895.0	Country	1	6183185.0	43788.0	3.0	0.0	512.0	1049399.0	6609.0	35.0	0.0	425.0	894199.0	5337.0	4780490.0	1.0	0.0	364.0	755300.0	4652.0	3212346.0	3.0	0.0	353.0	Country	287868.0	308456.0	363465.0	380886.0	305048.0	342176.0	284981.0	267042.0	276224.0	249830.0	229246.0	2174.0	2103.0	2227.0	2516.0	1866.0	2162.0	1682.0	1493.0	1718.0	1528.0	1406.0	1778201.0	1851673.0	1537177.0	1391640.0	1171651.0	1061246.0	979449.0	0.0	0.0	33.0	1.0	1.0	0.0	38.0	3.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	241.0	154.0	148.0	137.0	140.0	125.0	94.0	145.0	186.0	98.0	69.0	0.0	0.0	0.0	0.0	2764451.0	0.0	0.0	5290548.0	0.0	5893046.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
58949	13044497	It Ain't Safe	654060	It Ain't Safe (feat. Young Lord)	2014-11-02	Young L.O.R.D.	US	United States	462889.0	R&B/Soul	1	10064727.0	103088.0	57.0	57.0	7.0	320318.0	3709.0	-1.0	52.0	12.0	355076.0	3978.0	0.0	-1.0	-299.0	14.0	-9957411.0	-103770.0	0.0	3.0	411.0	1.0	Hip-Hop/Rap	145683.0	134290.0	108900.0	113421.0	97997.0	111785.0	130152.0	113139.0	0.0	10197058.0	0.0	1667.0	1427.0	1240.0	1447.0	1022.0	1097.0	1443.0	1438.0	0.0	106347.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	2.0	0.0	0.0	0.0	0.0	43.0	1.0	0.0	2.0	27.0	29.0	19.0	30.0	3.0	0.0	29.0	0.0	0.0	0.0	412.0	1.0	1.0	4.0	8.0	0.0	5.0	3.0	6.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	-11355988.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-161421121.0	-10503749.0	0.0	0.0	0.0	0.0	0.0	0.0	209.0	0.0	58.0	0.0	0.0	0.0	0.0
60636	18263822	Feed the Wolf	5677900	Antrenament în ritm ROCK	2020-02-07	Breaking Benjamin	US	United States	462898.0	Metal	1	9116709.0	79687.0	56.0	7.0	0.0	156959.0	997.0	-1.0	34.0	0.0	139795.0	926.0	0.0	-1.0	17.0	0.0	-9409151.0	-81600.0	0.0	1.0	-85.0	0.0	Hard Rock	62758.0	67163.0	52646.0	0.0	9269867.0	49350.0	48286.0	42159.0	46908.0	46397.0	0.0	416.0	416.0	273.0	0.0	80681.0	322.0	301.0	303.0	296.0	316.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	56.0	0.0	0.0	0.0	0.0	54.0	1.0	0.0	0.0	0.0	0.0	5.0	14.0	15.0	0.0	0.0	0.0	4.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3179720.0	0.0	6360227.0	7886131.0	-186752521.0	0.0	-121248313.0	0.0	0.0	0.0	6831261.0	8955636.0	0.0	-67396153.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	58.0	0.0	0.0	0.0	-108.0
64996	20320324	A Viagem	5407120	Coligações Expressivas 5	2019-11-22	Delacruz	BR	Brazil	462882.0	Pop	1	0.0	0.0	40.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	129633.0	-16.0	0.0	0.0	0.0	0.0	115112.0	2.0	0.0	0.0	Hip-Hop/Rap	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	49658.0	46590.0	42957.0	40086.0	40428.0	31148.0	43536.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	2.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
65665	15822714	A Night To Remember	992550	16 Biggest Hits	2002-02-19	Joe Diffie	US	United States	462895.0	Country	1	0.0	0.0	18.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-18.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	Country	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	17.0	0.0	16.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	26.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
67017	15533794	Before The Next Teardrop Falls	10080226	At His Best	2017-02-17	Freddy Fender	US	United States	462895.0	Country	1	18649702.0	80681.0	47.0	599.0	7.0	285961.0	2461.0	1.0	127.0	12.0	921379.0	9896.0	465127.0	-48.0	-724.0	107.0	-9326339.0	-11624.0	475290.0	1.0	862.0	80.0	Country	0.0	13048975.0	0.0	583671.0	11697339.0	0.0	11338875.0	313214.0	233313.0	183351.0	0.0	0.0	39505.0	0.0	4675.0	29772.0	0.0	28100.0	3352.0	2295.0	1618.0	0.0	136161.0	132625.0	166569.0	165933.0	167152.0	151611.0	156527.0	0.0	2.0	0.0	1.0	1.0	0.0	0.0	18.0	1.0	0.0	51.0	60.0	28.0	60.0	39.0	0.0	0.0	1.0	2.0	858.0	2.0	1.0	1.0	0.0	0.0	12.0	40.0	39.0	28.0	29.0	33.0	18.0	0.0	0.0	-26472206.0	15868197.0	0.0	4184206.0	0.0	0.0	0.0	0.0	4990588.0	0.0	16274892.0	4291958.0	0.0	0.0	0.0	-411.0	0.0	0.0	481.0	-7549.0	0.0	0.0	-193.0	0.0	0.0	611.0
68014	16091566	Pa Que la Pases Bien	178794	Luny Tunes Presents: Calle 434	2008-12-22	Arcangel	US	United States	462951.0	Latin	1	8863694.0	50108.0	39.0	0.0	114.0	524312.0	2004.0	-1.0	0.0	37.0	736065.0	3783.0	0.0	-38.0	0.0	0.0	-9292464.0	-52353.0	0.0	0.0	0.0	0.0	Latin	379605.0	426509.0	189966.0	176681.0	157665.0	208350.0	234010.0	293705.0	747482.0	653989.0	0.0	1718.0	1700.0	590.0	744.0	670.0	958.0	1182.0	1643.0	4237.0	2768.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	38.0	36.0	36.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-286303200.0	0.0	5740883.0	0.0	0.0	0.0	2334021.0	8435205.0	-3592132.0	0.0	-10837478.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
68448	12453884	Anywhere Is	1181464	Themes From "Calmi Cuori Appassionati"	2001-01-01	Enya	IE	Ireland	462884.0	Rock	1	8710094.0	43124.0	13.0	117.0	0.0	396478.0	2744.0	7.0	-87.0	0.0	403303.0	3017.0	0.0	-47.0	-43.0	0.0	-9420495.0	-48887.0	0.0	0.0	42.0	0.0	Celtic	140152.0	158353.0	128693.0	147469.0	120316.0	134555.0	148556.0	120192.0	137262.0	145513.0	0.0	882.0	1059.0	834.0	1055.0	855.0	984.0	1136.0	897.0	1036.0	1010.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	13.0	0.0	8.0	0.0	0.0	0.0	0.0	0.0	0.0	105.0	8.0	0.0	50.0	0.0	0.0	39.0	0.0	7.0	1.0	34.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	7949127.0	0.0	6858635.0	0.0	0.0	0.0	0.0	0.0	8650075.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-4862.0	13.0	0.0	0.0	0.0	0.0	0.0	0.0	128.0
72698	11082625	Tudo Que Vai	5977056	Cantando na Janela	2020-05-01	Capital Inicial	BR	Brazil	462884.0	Rock	1	10892326.0	37822.0	3.0	0.0	230.0	285557.0	1806.0	0.0	0.0	139.0	244958.0	1630.0	0.0	0.0	0.0	80.0	-10624535.0	-41060.0	0.0	3.0	0.0	203.0	Rock	9779907.0	121290.0	0.0	10308690.0	88425.0	81528.0	90749.0	72681.0	68116.0	67778.0	0.0	36926.0	739.0	0.0	39011.0	549.0	578.0	614.0	438.0	393.0	346.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	1.0	0.0	43.0	2.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	65.0	81.0	59.0	61.0	19.0	22.0	17.0	41.0	76.0	67.0	60.0	0.0	0.0	0.0	0.0	0.0	0.0	1754178.0	9617456.0	0.0	0.0	5307770.0	9206577.0	-5504174.0	-11295301.0	10730825.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
74631	12471868	A Night To Remember	17060505	A Night To Remember	2013-06-28	Shalamar	US	United States	462889.0	R&B/Soul	1	9941035.0	78496.0	28.0	121.0	1110.0	524586.0	5724.0	20.0	1350.0	1244.0	562338.0	5777.0	0.0	-1.0	56.0	1627.0	-10729760.0	-89649.0	0.0	-1.0	120.0	1118.0	R&B/Soul	2532515.0	212668.0	0.0	10261771.0	172108.0	177196.0	200923.0	184219.0	0.0	11125218.0	0.0	24630.0	2128.0	0.0	83873.0	1926.0	1886.0	2192.0	1699.0	0.0	93648.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	24.0	0.0	20.0	1.0	0.0	41.0	2.0	0.0	0.0	55.0	35.0	27.0	33.0	1290.0	12.0	6.0	38.0	0.0	0.0	0.0	336.0	379.0	400.0	423.0	421.0	724.0	510.0	393.0	407.0	336.0	375.0	-9833244.0	0.0	-151270.0	-9316419.0	5229079.0	0.0	0.0	-10925214.0	0.0	0.0	-242256030.0	0.0	-162550232.0	-9746483.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-14406.0	0.0	-10790.0	-3253.0	0.0
75912	21627288	Not A Damn Thing Changed	3382485	3 (The Purple Album)	2018-10-26	Lukas Graham	DK	Denmark	462882.0	Pop	1	10610617.0	144761.0	2.0	11.0	12.0	545929.0	5352.0	3.0	4.0	12.0	494267.0	3763.0	0.0	-25.0	10.0	10.0	-10982736.0	-153820.0	0.0	2.0	-2.0	10.0	Pop	164465.0	192215.0	147358.0	216876.0	181695.0	167290.0	176618.0	150359.0	0.0	551.0	488.0	1132.0	1568.0	1088.0	2557.0	1707.0	1368.0	1246.0	1149.0	0.0	1.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	3.0	1.0	22.0	1.0	2.0	22.0	0.0	0.0	8.0	2.0	3.0	0.0	4.0	0.0	0.0	0.0	0.0	1.0	4.0	4.0	5.0	3.0	4.0	4.0	4.0	2.0	4.0	4.0	2.0	0.0	0.0	-12096816.0	-23976318.0	-3489619.0	435346.0	0.0	0.0	0.0	0.0	10399082.0	7934799.0	0.0	-34803124.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-94.0	-10.0	0.0	-1784.0	0.0
81194	15717316	Resolve	1840280	In Your Honor	2005-06-14	Foo Fighters	US	United States	462882.0	Pop	1	11572091.0	32436.0	45.0	0.0	31.0	227770.0	1087.0	-1.0	0.0	35.0	210660.0	1027.0	0.0	-44.0	0.0	73.0	-11660351.0	-33663.0	0.0	0.0	0.0	88.0	Alternative	81514.0	84737.0	74172.0	90486.0	63112.0	77268.0	71915.0	61477.0	66104.0	76867.0	0.0	380.0	404.0	347.0	443.0	297.0	386.0	324.0	317.0	296.0	387.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	45.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	11.0	8.0	16.0	10.0	9.0	13.0	34.0	26.0	34.0	28.0	26.0	0.0	-10343994.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	11358918.0	-57857432.0	3018120.0	968820.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
82604	13248590	Il bene	535159	Scriverò il tuo nome (Deluxe Edition)	2016-04-15	Francesco Renga	IT	Italy	462882.0	Pop	1	9717953.0	40576.0	7.0	465.0	14.0	251305.0	902.0	-3.0	165.0	8.0	147709.0	764.0	299737.0	0.0	67.0	10.0	-10113267.0	-42239.0	342394.0	0.0	162.0	10.0	Alternative	172429.0	191238.0	113551.0	89047.0	48707.0	44040.0	53591.0	50078.0	66638.0	55045.0	0.0	1176.0	970.0	415.0	302.0	185.0	207.0	273.0	284.0	322.0	274.0	0.0	102314.0	89151.0	109748.0	100838.0	121175.0	105636.0	115583.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	269.0	102.0	48.0	15.0	0.0	38.0	33.0	67.0	59.0	36.0	4.0	3.0	3.0	4.0	1.0	4.0	3.0	3.0	4.0	3.0	3.0	0.0	0.0	0.0	-4465392.0	5788528.0	0.0	0.0	0.0	0.0	0.0	9599599.0	5920242.0	4711862.0	0.0	0.0	0.0	0.0	0.0	0.0	635.0	0.0	-91782.0	0.0	0.0	-600.0	1091.0	0.0	0.0
87816	15763460	Lady Marmalade	1025073	Kiss FM. Vive la Mejor Música de Todos los Tie...	2014-11-24	Patti LaBelle	US	United States	462889.0	R&B/Soul	1	8208174.0	77642.0	58.0	32.0	0.0	251017.0	4342.0	-1.0	17.0	22.0	326383.0	6885.0	2657357.0	-2.0	-87.0	335.0	-8781973.0	-88861.0	2622223.0	1.0	114.0	347.0	R&B/Soul	71965.0	94592.0	0.0	7660552.0	87428.0	108231.0	101819.0	116333.0	84980.0	71843.0	0.0	1101.0	1592.0	0.0	75418.0	1783.0	2656.0	2229.0	2000.0	1526.0	1066.0	0.0	843858.0	921538.0	873863.0	861956.0	930960.0	873451.0	817812.0	0.0	47.0	0.0	0.0	0.0	0.0	43.0	1.0	0.0	0.0	0.0	0.0	6.0	6.0	5.0	0.0	98.0	0.0	5.0	3.0	106.0	0.0	0.0	0.0	0.0	22.0	112.0	108.0	115.0	102.0	112.0	133.0	-29090215.0	0.0	0.0	0.0	7736644.0	-73686800.0	0.0	0.0	0.0	-48161864.0	-90578662.0	0.0	0.0	0.0	4085148.0	0.0	0.0	0.0	0.0	0.0	-45.0	0.0	0.0	-42769.0	121.0	0.0	-682.0	-204.0
91692	15843518	Down in the Valley	4005880	The Head and the Heart	2010-01-01	The Head And The Heart	US	United States	462882.0	Pop	1	10033090.0	56101.0	60.0	13.0	22.0	170180.0	1211.0	-1.0	29.0	22.0	167975.0	1564.0	0.0	-8.0	-179.0	17.0	-10364967.0	-58868.0	0.0	-1.0	3.0	23.0	Alternative	59531.0	67911.0	56778.0	65509.0	47893.0	51455.0	56846.0	59674.0	62600.0	54049.0	0.0	389.0	472.0	340.0	535.0	336.0	537.0	573.0	454.0	448.0	399.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	589673.0	29.0	3.0	0.0	0.0	0.0	0.0	30.0	0.0	50.0	0.0	0.0	8.0	3.0	11.0	15.0	0.0	16.0	0.0	0.0	0.0	0.0	6.0	5.0	9.0	8.0	5.0	5.0	6.0	6.0	6.0	8.0	9.0	0.0	-9247808.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-494383923.0	0.0	0.0	2452030.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	141.0	0.0	0.0	0.0	32.0	146.0	0.0
92198	15815646	John Deere Green	3953763	Honky Tonk Attitude	1993-04-20	Joe Diffie	US	United States	462895.0	Country	1	9050732.0	34479.0	4.0	1227.0	510.0	128628.0	871.0	1.0	973.0	391.0	97107.0	678.0	0.0	-5.0	983.0	1.0	-9271143.0	-36005.0	0.0	-3.0	759.0	1.0	Country	38944.0	49931.0	45271.0	0.0	9174841.0	0.0	9239958.0	28065.0	31129.0	24758.0	0.0	290.0	331.0	304.0	0.0	35343.0	0.0	35755.0	190.0	256.0	177.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	368.0	404.0	282.0	390.0	301.0	317.0	313.0	353.0	357.0	234.0	168.0	241.0	152.0	149.0	138.0	104.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	8228140.0	0.0	0.0	0.0	0.0	0.0	-633046.0	-9527055.0	8729984.0	-6075022.0	-815775.0	0.0	0.0	-4978.0	0.0	0.0	5121.0	0.0	0.0	5466.0	3744.0	0.0	5131.0	5583.0	0.0	0.0
94922	13250427	La donna cannone	3911484	La Donna Cannone	1983-01-01	Francesco De Gregori	IT	Italy	462884.0	Rock	1	17344290.0	53891.0	58.0	0.0	125.0	657634.0	2926.0	0.0	0.0	135.0	527329.0	2455.0	1464351.0	-2.0	0.0	102.0	-11418802.0	-17637.0	1266778.0	1.0	0.0	123.0	Pop	228258.0	243892.0	0.0	17817511.0	141338.0	167272.0	197446.0	162611.0	208059.0	0.0	129403.0	801.0	972.0	0.0	55994.0	642.0	767.0	960.0	728.0	898.0	0.0	702.0	512366.0	483930.0	539732.0	440689.0	432836.0	427431.0	406511.0	0.0	34.0	0.0	0.0	0.0	0.0	31.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	40.0	38.0	54.0	40.0	41.0	31.0	37.0	34.0	39.0	38.0	46.0	-1019162.0	15698797.0	0.0	10667636.0	0.0	0.0	0.0	0.0	0.0	17121084.0	0.0	-199242968.0	-9941924.0	0.0	17434874.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_PLAYS_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_PLAYS_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M1_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M1_YOUTUBE_LIKES_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M1_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_PLAYS_GROWTH	M3_SPOTIFY_PLAYS_GROWTH	M4_SPOTIFY_PLAYS_GROWTH	M5_SPOTIFY_PLAYS_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M1_SPOTIFY_POPULARITY_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M1_TIKTOK_POSTS_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M1_AIRPLAY_STREAMS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK1_SPOTIFY_PLAYS_GROWTH	WK2_SPOTIFY_PLAYS_GROWTH	WK3_SPOTIFY_PLAYS_GROWTH	WK4_SPOTIFY_PLAYS_GROWTH	WK5_SPOTIFY_PLAYS_GROWTH	WK6_SPOTIFY_PLAYS_GROWTH	WK7_SPOTIFY_PLAYS_GROWTH	WK8_SPOTIFY_PLAYS_GROWTH	WK9_SPOTIFY_PLAYS_GROWTH	WK10_SPOTIFY_PLAYS_GROWTH	WK11_SPOTIFY_PLAYS_GROWTH	WK12_SPOTIFY_PLAYS_GROWTH	WK13_SPOTIFY_PLAYS_GROWTH	WK14_SPOTIFY_PLAYS_GROWTH	WK15_SPOTIFY_PLAYS_GROWTH	WK1_TIKTOK_POSTS_GROWTH	WK2_TIKTOK_POSTS_GROWTH	WK3_TIKTOK_POSTS_GROWTH	WK4_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
1106	63955242	Jingle Bell Rock	16515193	Jingle Bell Rock - Single	2021-10-15	Aaron Goodvin	US	United States	462895.0	Country	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	Country	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	371624.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1637	12760478	Walk of Life	3320514	Sultans Of Swing - The Very Best Of Dire Strai...	1998-11-10	Dire Straits	GB	United Kingdom	462941.0	Hard Rock	1	190232534.0	47301.0	0.0	37.0	121.0	4754.0	9050473.0	44976.0	0.0	9.0	49.0	4968.0	8877656.0	48117.0	23219669.0	-1.0	-447.0	5008.0	9129411.0	50740.0	22342960.0	2.0	616.0	4755.0	Hard Rock	0.0	3135347.0	3411246.0	3090998.0	3156404.0	2803071.0	3015935.0	3065314.0	2796407.0	2931368.0	2859852.0	3338191.0	0.0	15368.0	16634.0	14775.0	15715.0	14486.0	16457.0	16569.0	15091.0	16487.0	16006.0	18247.0	0.0	0.0	0.0	0.0	0.0	7363529.0	7716476.0	8590867.0	6912326.0	7726198.0	7430251.0	7186511.0	0.0	0.0	2.0	0.0	7.0	2.0	0.0	0.0	0.0	2.0	0.0	1.0	0.0	67.0	32.0	25.0	15.0	9.0	0.0	390.0	0.0	515.0	39.0	62.0	0.0	1487.0	1637.0	1653.0	1692.0	1623.0	1707.0	1675.0	1626.0	1611.0	1504.0	1640.0	0.0	0.0	0.0	183103054.0	0.0	0.0	184715830.0	186223982.0	0.0	0.0	188884632.0	0.0	0.0	0.0	189235476.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-284.0	0.0	0.0	0.0	0.0	373.0	0.0	-1126.0	0.0	-8637.0	0.0	548.0
1915	49664963	Vibe	11676166	Vibe	2021-06-18	Mike Candys	CH	Switzerland	462885.0	Dance	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	67.0	0.0	6.0	0.0	0.0	6609606.0	1.0	0.0	16.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2960144.0	2606502.0	2398479.0	2055240.0	2155887.0	0.0	0.0	0.0	0.0	0.0	0.0	64.0	3.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	14.0	0.0	0.0	27.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	0.0	6.0	9.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1945	28766256	Suspicious Minds	7942716	Suspicious Minds	2020-03-15	Flavio	ES	Spain	462958.0	Latin Hip-Hop/Rap	1	0.0	0.0	0.0	2.0	1.0	0.0	0.0	0.0	0.0	20.0	0.0	0.0	0.0	0.0	0.0	-5.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	Latin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	18.0	0.0	3.0	0.0	15.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-212.0	8.0	0.0	0.0	-22084.0	0.0
2231	15682717	Islands In the Stream	1847470	Elokuvien 100 suosituinta	2010-02-08	Kenny Rogers	US	United States	462895.0	Country	1	16525310.0	81813.0	0.0	69.0	9.0	2548.0	4749886.0	23413.0	0.0	-1.0	-15.0	1866.0	4317447.0	23861.0	14031108.0	-2.0	-1149.0	1820.0	4325202.0	22869.0	13675769.0	2.0	1291.0	1477.0	Country	0.0	0.0	11292080.0	0.0	47682.0	15809923.0	0.0	18495497.0	1554832.0	1591491.0	1362253.0	1371458.0	0.0	0.0	34427.0	0.0	594.0	54849.0	0.0	68954.0	8747.0	8212.0	7290.0	7367.0	0.0	0.0	0.0	0.0	0.0	4483582.0	4752343.0	4547152.0	4731613.0	4700026.0	4581506.0	4394237.0	0.0	17.0	52.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	1.0	4.0	0.0	0.0	0.0	1.0	138.0	0.0	1430.0	0.0	0.0	767.0	897.0	872.0	452.0	542.0	578.0	579.0	663.0	617.0	504.0	356.0	0.0	0.0	0.0	0.0	-125658211.0	0.0	8396664.0	-649593937.0	0.0	0.0	0.0	0.0	15761423.0	0.0	4949215.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1334.0	0.0	1159.0	0.0	0.0	0.0	0.0
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101911	59705222	Vibe	20597269	Haunted Haus	2022-04-08	Ghastly	US	United States	462890.0	House	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	413392.0	0.0	0.0	2.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	168078.0	132621.0	112693.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
101950	31948328	Forever	6999651	Forever	2020-11-13	rei brown	US	United States	462882.0	Pop	1	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	0.0	0.0	502808.0	0.0	-2.0	0.0	0.0	0.0	635995.0	0.0	10.0	0.0	Electronic	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	172341.0	169746.0	170566.0	162496.0	203819.0	202456.0	229720.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	2.0	1.0	3.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-10.0	0.0	0.0	0.0	0.0	11.0
101971	51828537	O Ex da Sua Vida	12128789	Falando de Amor, Vol. 2	2021-07-01	Gusttavo Lima	BR	Brazil	462951.0	Latin	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	12996547.0	1.0	135.0	145.0	Latin	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	7108849.0	5616118.0	4726408.0	3516757.0	4753382.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	212.0	90.0	58.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	16.0	0.0	17.0	72.0	56.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102020	32013076	The Best Days (feat. Tabitha)	7065746	Home Sweet Home EP	2020-11-27	Sam Feldt	NL	Netherlands	462885.0	Dance	1	0.0	0.0	0.0	45.0	8.0	43.0	0.0	0.0	0.0	-2.0	39.0	25.0	0.0	0.0	9417866.0	-4.0	0.0	7.0	0.0	0.0	4777785.0	-1.0	0.0	8.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3622410.0	3382095.0	3607489.0	2428282.0	1817046.0	1555659.0	1405080.0	0.0	0.0	2.0	0.0	47.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	9.0	13.0	17.0	18.0	12.0	0.0	0.0	5.0	6.0	0.0	9.0	5.0	9.0	9.0	7.0	4.0	1.0	2.0	2.0	2.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	130.0	0.0	0.0	-11.0	105.0	-3616.0	0.0	115.0
102021	49090118	Pegador	11528373	Pegador	2021-06-12	João Bosco & Vinicius	BR	Brazil	462877.0	Sertanejo	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5526599.0	0.0	0.0	8.0	465271.0	2780.0	1587118.0	-7.0	0.0	0.0	Jazz	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	157237.0	143648.0	164386.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1015.0	858.0	907.0	0.0	0.0	0.0	0.0	0.0	0.0	2490112.0	1755999.0	1280488.0	779232.0	428865.0	379021.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	7.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_PLAYS_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_PLAYS_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M1_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M1_YOUTUBE_LIKES_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M1_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_PLAYS_GROWTH	M3_SPOTIFY_PLAYS_GROWTH	M4_SPOTIFY_PLAYS_GROWTH	M5_SPOTIFY_PLAYS_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M1_SPOTIFY_POPULARITY_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M1_TIKTOK_POSTS_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M1_AIRPLAY_STREAMS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK1_SPOTIFY_PLAYS_GROWTH	WK2_SPOTIFY_PLAYS_GROWTH	WK3_SPOTIFY_PLAYS_GROWTH	WK4_SPOTIFY_PLAYS_GROWTH	WK5_SPOTIFY_PLAYS_GROWTH	WK6_SPOTIFY_PLAYS_GROWTH	WK7_SPOTIFY_PLAYS_GROWTH	WK8_SPOTIFY_PLAYS_GROWTH	WK9_SPOTIFY_PLAYS_GROWTH	WK10_SPOTIFY_PLAYS_GROWTH	WK11_SPOTIFY_PLAYS_GROWTH	WK12_SPOTIFY_PLAYS_GROWTH	WK13_SPOTIFY_PLAYS_GROWTH	WK14_SPOTIFY_PLAYS_GROWTH	WK15_SPOTIFY_PLAYS_GROWTH	WK1_TIKTOK_POSTS_GROWTH	WK2_TIKTOK_POSTS_GROWTH	WK3_TIKTOK_POSTS_GROWTH	WK4_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
6	33741047	Over	12527653	Afterhour	2021-05-14	FOURTY	DE	Germany	462883.0	Hip-Hop/Rap	1	0.0	0.0	0.0	30.0	0.0	12.0	0.0	0.0	0.0	-9.0	0.0	81.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	75.0	3.0	3.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-845.0
15	11657825	Nocturne No.1 In B Flat Minor, Op.9 No.1	12375744	Classical For Gardening	2021-07-09	Frédéric Chopin	PL	Poland	462894.0	New Age	1	0.0	0.0	0.0	60.0	0.0	9.0	0.0	0.0	0.0	-1.0	0.0	12.0	0.0	0.0	2598859.0	2.0	0.0	4.0	0.0	0.0	3278214.0	3.0	0.0	8.0	Classical	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	605496.0	730399.0	855677.0	1012783.0	1139428.0	1099183.0	1039603.0	0.0	1.0	1.0	0.0	0.0	40.0	0.0	0.0	42.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	5.0	4.0	3.0	2.0	2.0	0.0	1.0	5.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
23	32875570	Lonely (feat. Malou)	12555699	Lonely (feat. Malou) - Single	2021-02-19	Malou	DE	Germany	462885.0	Dance	1	0.0	0.0	0.0	53.0	3.0	21.0	0.0	0.0	0.0	-3.0	2.0	29.0	0.0	0.0	380828.0	0.0	2.0	16.0	0.0	0.0	194705.0	0.0	1.0	18.0	Dance	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	172136.0	130752.0	132138.0	117938.0	90096.0	56026.0	48583.0	0.0	0.0	53.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	8.0	11.0	13.0	5.0	1.0	2.0	13.0	13.0	3.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-84.0	-322.0	0.0	13.0
27	15966354	Flex	147413	Flex (feat. Tory Lanez & Fabolous) - Single	2016-07-01	Joe Budden	US	United States	462883.0	Hip-Hop/Rap	1	1260510.0	88.0	0.0	2.0	0.0	0.0	7031.0	76.0	0.0	-1.0	0.0	0.0	6931.0	75.0	0.0	-3.0	0.0	0.0	5891.0	68.0	0.0	2.0	0.0	0.0	Hip-Hop/Rap	0.0	1848.0	2577.0	2534.0	2138.0	2359.0	2288.0	2168.0	2475.0	1527.0	2125.0	2239.0	0.0	38.0	25.0	23.0	27.0	26.0	22.0	25.0	28.0	21.0	22.0	25.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	1.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-9994086.0	0.0	-35213594.0	0.0	0.0	0.0	-7608523.0	-5207633.0	-5500156.0	949964.0	-344181195.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
32	20563402	To The End	6842991	Fall For Jesus	2020-10-09	Mack Brock	US	United States	462879.0	Christian	1	0.0	0.0	0.0	48.0	0.0	0.0	0.0	0.0	0.0	-2.0	0.0	0.0	0.0	0.0	275306.0	-46.0	0.0	0.0	0.0	0.0	333607.0	0.0	0.0	0.0	Christian	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	86467.0	90133.0	96965.0	88208.0	102820.0	112978.0	117809.0	0.0	0.0	42.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
102512	31721983	Underdog	6898771	Underdog	2020-10-19	Ummet Ozcan	NL	Netherlands	462890.0	House	1	0.0	0.0	0.0	0.0	0.0	21.0	0.0	0.0	0.0	-3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	Electronic	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102520	47079324	Harraga	11032308	Harraga	2021-06-03	YONII	AT	Austria	462883.0	Hip-Hop/Rap	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	358284.0	0.0	0.0	0.0	0.0	0.0	197077.0	0.0	2.0	0.0	Hip-Hop/Rap	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	167618.0	110978.0	79688.0	70854.0	57052.0	69171.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
102529	18618298	Over the Rainbow	961194	Christmas Hits 2007	2007-11-26	Eva Cassidy	US	United States	462893.0	Jazz	1	0.0	0.0	0.0	2.0	183.0	0.0	0.0	0.0	0.0	-1.0	36.0	0.0	0.0	0.0	963252.0	-52.0	-233.0	0.0	0.0	0.0	1032416.0	54.0	244.0	0.0	Folk	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	273709.0	300647.0	345797.0	316808.0	356964.0	348952.0	326500.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	53.0	0.0	1.0	0.0	148.0	20.0	24.0	3.0	9.0	0.0	241.0	0.0	3.0	37.0	204.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-15278.0	0.0	0.0	106.0	0.0	0.0	-421.0	-27908.0	195.0	0.0	203.0	-216.0	0.0
102530	33435556	44BABY	7728896	44BABY	2021-03-11	NARU	DE	Germany	462883.0	Hip-Hop/Rap	1	0.0	0.0	0.0	59.0	2.0	0.0	0.0	0.0	0.0	-10.0	-1.0	0.0	0.0	0.0	206363.0	-41.0	0.0	0.0	0.0	0.0	115913.0	3.0	0.0	0.0	Hip-Hop/Rap	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	121935.0	90967.0	63754.0	51642.0	43803.0	32884.0	39226.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-189.0	0.0	-435.0
102544	17119125	I'll Find My Way Home - Remastered	6939072	Christmas Chill	2020-10-30	Jon & Vangelis	GR	Greece	462882.0	Pop	1	0.0	0.0	0.0	28.0	0.0	0.0	0.0	0.0	0.0	0.0	7.0	37.0	0.0	0.0	755767.0	-34.0	-7.0	377.0	0.0	0.0	805288.0	10.0	25.0	377.0	New Age	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	264414.0	260644.0	257199.0	237924.0	261847.0	213170.0	330271.0	0.0	0.0	3.0	0.0	0.0	1.0	0.0	2.0	0.0	9.0	0.0	12.0	0.0	0.0	0.0	3.0	3.0	1.0	6.0	0.0	0.0	0.0	0.0	25.0	0.0	0.0	0.0	0.0	0.0	37.0	117.0	142.0	118.0	146.0	115.0	116.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-245.0	0.0	0.0	-18.0	-254.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_PLAYS_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M2_SPOTIFY_PLAYS_GROWTH	M3_SPOTIFY_PLAYS_GROWTH	M4_SPOTIFY_PLAYS_GROWTH	M5_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M8_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M11_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK8_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
523	15117472	Me And My B*tch	3964024	Ready To Die The Remaster	1994-09-13	The Notorious B.I.G.	US	United States	462883.0	Hip-Hop/Rap	1	34972.0	27.0	1084365.0	25.0	38.0	6.0	0.0	0.0	-4.0	28.0	5.0	0.0	0.0	-53.0	-162.0	0.0	2579.0	55.0	796096.0	53.0	199.0	0.0	Hip-Hop/Rap	1859.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	623.0	27.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	12.0	273562.0	544084.0	346690.0	316041.0	300789.0	246539.0	273199.0	261627.0	261270.0	1.0	5.0	0.0	0.0	19.0	0.0	51.0	53.0	0.0	0.0	17.0	18.0	8.0	6.0	14.0	84.0	198.0	1.0	0.0	6.0	0.0	3.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	33505.0	0.0	0.0	-1378.0	106.0	127.0	0.0	0.0	101.0
965	22381921	Mother	7747173	Novidades Rock	2021-03-12	The Amazons	GB	United Kingdom	462884.0	Rock	1	0.0	0.0	0.0	42.0	0.0	26.0	0.0	0.0	2.0	0.0	28.0	0.0	0.0	-44.0	0.0	44.0	0.0	0.0	0.0	1.0	0.0	30.0	Rock	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	42.0	1.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5.0	13.0	16.0	3.0	9.0	15.0	16.0	13.0	8.0	12.0	10.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3452	13069086	Glimmer	3082170	Given To The Wild	2012-04-24	The Maccabees	GB	United Kingdom	462888.0	Alternative	1	0.0	0.0	0.0	26.0	0.0	0.0	0.0	0.0	-1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	1.0	Alternative	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	2.0	0.0	0.0	28.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
12192	15895897	Light It Up	3254120	Black	2016-06-10	Dierks Bentley	US	United States	462895.0	Country	1	387204.0	0.0	0.0	37.0	0.0	0.0	3003.0	21.0	1.0	0.0	0.0	4000.0	21.0	0.0	0.0	0.0	3074.0	16.0	0.0	0.0	0.0	0.0	Country	0.0	918.0	977.0	1108.0	1723.0	1184.0	1093.0	880.0	1052.0	1142.0	0.0	6.0	3.0	12.0	8.0	2.0	11.0	6.0	6.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	2.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-187634654.0	-243830.0	0.0	0.0	0.0	-493.0	-4527.0	-25000.0
17004	17722240	Mother	2156928	Christmas Songs	2017-11-10	Love & The Outcome	US	United States	462879.0	Christian	1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	18.0	0.0	0.0	Christian	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	21.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK8_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK11_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK10_TIKTOK_POSTS_GROWTH	WK11_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
237	32328050	Eternal Flame	7187918	Eternal Flame	2020-12-18	PENTAGON	KR	South Korea	462882.0	Pop	1	0.0	0.0	50.0	0.0	49.0	0.0	0.0	-6.0	0.0	21.0	0.0	0.0	0.0	-44.0	9.0	31.0	0.0	0.0	0.0	0.0	0.0	33.0	K-Pop	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	5.0	0.0	0.0	0.0	0.0	8.0	6.0	5.0	9.0	7.0	12.0	9.0	10.0	12.0	8.0	13.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
509	21815691	Starlight	3365695	Starlight	2018-10-19	BABYMETAL	JP	Japan	462884.0	Rock	1	5179619.0	3264.0	22.0	47.0	4.0	156612.0	2625.0	24.0	20.0	0.0	153045.0	2502.0	0.0	-36.0	-62.0	5.0	129372.0	2046.0	0.0	16.0	73.0	4.0	Hard Rock	70830.0	75610.0	56308.0	56591.0	43713.0	51029.0	54787.0	47229.0	46026.0	42660.0	40686.0	1258.0	1409.0	909.0	978.0	738.0	901.0	868.0	733.0	751.0	654.0	641.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9.0	0.0	32.0	0.0	10.0	17.0	0.0	0.0	47.0	12.0	6.0	2.0	0.0	6.0	1.0	6.0	66.0	0.0	1.0	0.0	0.0	0.0	2.0	1.0	2.0	1.0	0.0	3.0	0.0	-308425708.0	0.0	-46136631.0	-5781012.0	-349708.0	0.0	1333673.0	-4050805.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-18748.0	0.0
2282	12451508	Starlight	6239135	100 Greatest Pride Songs	2020-07-01	Muse	GB	United Kingdom	462882.0	Pop	1	7131300.0	123392.0	68.0	466.0	4283.0	-6763894.0	-123030.0	-1.0	400.0	4461.0	9484508.0	148401.0	12179814.0	-2.0	-2364.0	3965.0	-9157858.0	-144628.0	12115074.0	3.0	1847.0	3818.0	Alternative	0.0	435058.0	0.0	7642535.0	0.0	0.0	9404042.0	442622.0	0.0	10234865.0	0.0	85349.0	4446.0	0.0	131494.0	0.0	0.0	147580.0	4135.0	0.0	154036.0	0.0	3942820.0	3796329.0	4306314.0	4077171.0	4169383.0	3925369.0	4020322.0	1.0	2.0	0.0	0.0	41.0	0.0	2.0	1.0	164.0	144.0	145.0	142.0	113.0	119.0	0.0	452.0	2396.0	0.0	1305.0	1552.0	1496.0	1531.0	1434.0	1366.0	1391.0	1208.0	1251.0	1268.0	1299.0	3788701.0	0.0	0.0	0.0	0.0	0.0	0.0	-9319832.0	6290037.0	3496380.0	5867007.0	903.0	0.0	0.0	1346.0	0.0	1944.0	2012.0
5178	15758732	Don't Bring Me Down	169825	Definitive Collection	1992-01-01	Electric Light Orchestra	GB	United Kingdom	462884.0	Rock	1	2706997.0	17577.0	3.0	406.0	2063.0	-2088818.0	-14917.0	21.0	385.0	2096.0	2885610.0	24149.0	13010157.0	-2.0	-1746.0	1984.0	571152.0	6038.0	12023829.0	2.0	2199.0	1846.0	Pop	0.0	2225506.0	0.0	35312.0	29089.0	2581973.0	160070.0	143567.0	0.0	3074658.0	214339.0	0.0	17206.0	0.0	653.0	457.0	20595.0	1800.0	1754.0	0.0	25036.0	2343.0	4316789.0	4587710.0	4549282.0	3873165.0	4425760.0	3899784.0	3698285.0	0.0	3.0	0.0	22.0	0.0	0.0	2.0	0.0	99.0	170.0	159.0	123.0	103.0	124.0	0.0	2078.0	0.0	0.0	666.0	722.0	713.0	712.0	671.0	667.0	651.0	666.0	685.0	583.0	578.0	0.0	0.0	1914827.0	0.0	1187855.0	0.0	197009.0	0.0	-8380023.0	0.0	2101057.0	930.0	-82.0	1173.0	0.0	1002.0	283.0	0.0
13528	33648905	Starlight	7780009	World's Most Stressed Out Gardener	2021-03-19	Chad VanGaalen	CA	Canada	462884.0	Rock	1	0.0	0.0	0.0	0.0	12.0	0.0	0.0	33.0	0.0	18.0	0.0	0.0	0.0	-20.0	0.0	8.0	0.0	0.0	0.0	1.0	1.0	8.0	Electronic	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	11879.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	42.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	10.0	8.0	0.0	2.0	5.0	1.0	2.0	1.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	CM_TRACK	TRACK_NAME	CM_ALBUM	ALBUM_NAME	RELEASE_DATE	ARTIST_NAME	ARTIST_COUNTRY_CODE2	COUNTRY_NAME	GENRE_ID	ARTIST_GENRE	DUPE	Q1_YOUTUBE_VIEWS_GROWTH	Q1_YOUTUBE_LIKES_GROWTH	Q1_SPOTIFY_POPULARITY_GROWTH	Q1_TIKTOK_POSTS_GROWTH	Q1_AIRPLAY_STREAMS_GROWTH	Q2_YOUTUBE_VIEWS_GROWTH	Q2_YOUTUBE_LIKES_GROWTH	Q2_SPOTIFY_POPULARITY_GROWTH	Q2_TIKTOK_POSTS_GROWTH	Q2_AIRPLAY_STREAMS_GROWTH	Q3_YOUTUBE_VIEWS_GROWTH	Q3_YOUTUBE_LIKES_GROWTH	Q3_SPOTIFY_PLAYS_GROWTH	Q3_SPOTIFY_POPULARITY_GROWTH	Q3_TIKTOK_POSTS_GROWTH	Q3_AIRPLAY_STREAMS_GROWTH	Q4_YOUTUBE_VIEWS_GROWTH	Q4_YOUTUBE_LIKES_GROWTH	Q4_SPOTIFY_PLAYS_GROWTH	Q4_SPOTIFY_POPULARITY_GROWTH	Q4_TIKTOK_POSTS_GROWTH	Q4_AIRPLAY_STREAMS_GROWTH	GENRE	M2_YOUTUBE_VIEWS_GROWTH	M3_YOUTUBE_VIEWS_GROWTH	M4_YOUTUBE_VIEWS_GROWTH	M5_YOUTUBE_VIEWS_GROWTH	M6_YOUTUBE_VIEWS_GROWTH	M7_YOUTUBE_VIEWS_GROWTH	M8_YOUTUBE_VIEWS_GROWTH	M9_YOUTUBE_VIEWS_GROWTH	M10_YOUTUBE_VIEWS_GROWTH	M11_YOUTUBE_VIEWS_GROWTH	M12_YOUTUBE_VIEWS_GROWTH	M2_YOUTUBE_LIKES_GROWTH	M3_YOUTUBE_LIKES_GROWTH	M4_YOUTUBE_LIKES_GROWTH	M5_YOUTUBE_LIKES_GROWTH	M6_YOUTUBE_LIKES_GROWTH	M7_YOUTUBE_LIKES_GROWTH	M8_YOUTUBE_LIKES_GROWTH	M9_YOUTUBE_LIKES_GROWTH	M10_YOUTUBE_LIKES_GROWTH	M11_YOUTUBE_LIKES_GROWTH	M12_YOUTUBE_LIKES_GROWTH	M6_SPOTIFY_PLAYS_GROWTH	M7_SPOTIFY_PLAYS_GROWTH	M8_SPOTIFY_PLAYS_GROWTH	M9_SPOTIFY_PLAYS_GROWTH	M10_SPOTIFY_PLAYS_GROWTH	M11_SPOTIFY_PLAYS_GROWTH	M12_SPOTIFY_PLAYS_GROWTH	M2_SPOTIFY_POPULARITY_GROWTH	M3_SPOTIFY_POPULARITY_GROWTH	M4_SPOTIFY_POPULARITY_GROWTH	M5_SPOTIFY_POPULARITY_GROWTH	M6_SPOTIFY_POPULARITY_GROWTH	M7_SPOTIFY_POPULARITY_GROWTH	M9_SPOTIFY_POPULARITY_GROWTH	M10_SPOTIFY_POPULARITY_GROWTH	M12_SPOTIFY_POPULARITY_GROWTH	M2_TIKTOK_POSTS_GROWTH	M3_TIKTOK_POSTS_GROWTH	M4_TIKTOK_POSTS_GROWTH	M5_TIKTOK_POSTS_GROWTH	M6_TIKTOK_POSTS_GROWTH	M7_TIKTOK_POSTS_GROWTH	M8_TIKTOK_POSTS_GROWTH	M9_TIKTOK_POSTS_GROWTH	M10_TIKTOK_POSTS_GROWTH	M11_TIKTOK_POSTS_GROWTH	M12_TIKTOK_POSTS_GROWTH	M2_AIRPLAY_STREAMS_GROWTH	M3_AIRPLAY_STREAMS_GROWTH	M4_AIRPLAY_STREAMS_GROWTH	M5_AIRPLAY_STREAMS_GROWTH	M6_AIRPLAY_STREAMS_GROWTH	M7_AIRPLAY_STREAMS_GROWTH	M8_AIRPLAY_STREAMS_GROWTH	M9_AIRPLAY_STREAMS_GROWTH	M10_AIRPLAY_STREAMS_GROWTH	M11_AIRPLAY_STREAMS_GROWTH	M12_AIRPLAY_STREAMS_GROWTH	WK1_YT_VIEWS_GROWTH	WK2_YT_VIEWS_GROWTH	WK3_YT_VIEWS_GROWTH	WK4_YT_VIEWS_GROWTH	WK5_YT_VIEWS_GROWTH	WK6_YT_VIEWS_GROWTH	WK7_YT_VIEWS_GROWTH	WK9_YT_VIEWS_GROWTH	WK10_YT_VIEWS_GROWTH	WK12_YT_VIEWS_GROWTH	WK13_YT_VIEWS_GROWTH	WK14_YT_VIEWS_GROWTH	WK15_YT_VIEWS_GROWTH	WK3_TIKTOK_POSTS_GROWTH	WK5_TIKTOK_POSTS_GROWTH	WK6_TIKTOK_POSTS_GROWTH	WK7_TIKTOK_POSTS_GROWTH	WK9_TIKTOK_POSTS_GROWTH	WK12_TIKTOK_POSTS_GROWTH	WK13_TIKTOK_POSTS_GROWTH	WK14_TIKTOK_POSTS_GROWTH	WK15_TIKTOK_POSTS_GROWTH
4466	13436558	Por Mujeres Como Tú	18609450	Rolitas Bien Padres	2022-01-26	Pepe Aguilar	MX	Mexico	462957.0	Latin Pop	1	154277064.0	36166.0	72.0	186.0	73.0	8472815.0	30311.0	-63.0	422.0	77.0	6386299.0	25554.0	9594220.0	61.0	558.0	87.0	6756783.0	33528.0	10588867.0	4.0	303.0	79.0	Latin	3655001.0	3904724.0	3308060.0	3002850.0	2161905.0	2395815.0	2165736.0	1824748.0	1871438.0	1985950.0	2899395.0	10726.0	12404.0	10952.0	10852.0	8507.0	8961.0	8454.0	8139.0	8460.0	10152.0	14916.0	2998733.0	3084653.0	3110529.0	3399038.0	3503354.0	3199483.0	3886030.0	0.0	72.0	0.0	0.0	5.0	61.0	1.0	2.0	3.0	0.0	149.0	90.0	150.0	182.0	0.0	997.0	176.0	0.0	1378.0	123.0	25.0	40.0	22.0	23.0	32.0	30.0	31.0	26.0	26.0	28.0	25.0	0.0	117396701.0	126393135.0	0.0	0.0	147087224.0	0.0	-187033377.0	0.0	0.0	0.0	0.0	155602536.0	0.0	0.0	0.0	0.0	-2762.0	0.0	0.0	0.0	203.0
5526	13300817	Dragostea Din Tei	1237695	DiscO-Zone	2002-01-01	O-Zone	MD	Moldova	462885.0	Dance	1	81004429.0	934846.0	59.0	315.0	1269.0	13417071.0	156665.0	-11.0	273.0	1308.0	13350982.0	146929.0	12009520.0	7.0	236.0	1141.0	11323738.0	118562.0	10558977.0	2.0	135.0	602.0	Dance	13778871.0	5888334.0	5036098.0	4478335.0	3902638.0	0.0	3500532.0	104266207.0	3409393.0	4055780.0	3858565.0	591299.0	87374.0	57318.0	53618.0	45729.0	0.0	61999.0	1207617.0	34721.0	44889.0	38952.0	4098314.0	4029713.0	4377800.0	3602007.0	3497895.0	3379441.0	3681641.0	12.0	3.0	0.0	0.0	0.0	9.0	55.0	2.0	1.0	103.0	144.0	129.0	93.0	51.0	71.0	111.0	54.0	0.0	0.0	2067.0	407.0	530.0	467.0	393.0	448.0	395.0	428.0	318.0	218.0	182.0	202.0	-3313122.0	56155653.0	59374575.0	0.0	31692149.0	0.0	-18077542.0	0.0	75342819.0	0.0	57100892.0	0.0	80795174.0	1050.0	0.0	0.0	1169.0	0.0	1380.0	1174.0	-4558.0	1043.0
5955	15818423	Ready or Not	1619765	Greatest Hits	2003-03-25	Fugees	US	United States	462883.0	Hip-Hop/Rap	1	166041595.0	49007.0	22.0	1608.0	2239.0	6022236.0	50845.0	24.0	433.0	2765.0	5402937.0	45821.0	10520248.0	-2.0	2173.0	17349.0	5051890.0	46636.0	10483224.0	2.0	3525.0	2654.0	Hip-Hop/Rap	1995827.0	2327243.0	2178731.0	2024028.0	1819477.0	0.0	159754688.0	1650192.0	0.0	162650465.0	1601884.0	14570.0	20056.0	18086.0	16837.0	15922.0	0.0	703345.0	14762.0	0.0	718995.0	16811.0	3458158.0	3405455.0	3760670.0	3354123.0	3750006.0	3326287.0	3406931.0	0.0	3.0	0.0	25.0	0.0	0.0	0.0	2.0	0.0	6.0	332.0	0.0	630.0	130.0	584.0	835.0	754.0	1257.0	1219.0	1049.0	667.0	789.0	818.0	1163.0	784.0	907.0	892.0	15550.0	989.0	837.0	828.0	0.0	0.0	0.0	132189738.0	0.0	160834196.0	-163537479.0	0.0	0.0	-448116296.0	0.0	164440535.0	0.0	0.0	-2498096.0	1841.0	-267396.0	0.0	-6010.0	0.0	1842.0	2273.0
15229	15812253	It's Raining Men	5088176	NOW That's What I Call Music! 3	2019-07-19	The Weather Girls	US	United States	462882.0	Pop	1	0.0	0.0	53.0	0.0	2300.0	0.0	0.0	7.0	0.0	1922.0	0.0	0.0	11245886.0	-1.0	0.0	287.0	0.0	0.0	10517458.0	2.0	0.0	1039.0	Electronic	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3935463.0	3553331.0	4135340.0	3557215.0	3586905.0	3320198.0	3610355.0	0.0	1.0	0.0	8.0	0.0	0.0	31.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	735.0	783.0	734.0	627.0	561.0	90.0	109.0	88.0	79.0	873.0	87.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
21133	15812112	It's Raining Men	3952673	Success	1982-01-01	The Weather Girls	US	United States	462889.0	R&B/Soul	1	0.0	0.0	15.0	0.0	309.0	0.0	0.0	41.0	0.0	317.0	0.0	0.0	2282069.0	1.0	0.0	279.0	0.0	0.0	2828489.0	5.0	0.0	230.0	Electronic	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	630073.0	667773.0	825450.0	788846.0	930638.0	879615.0	1018236.0	0.0	0.0	10.0	29.0	2.0	0.0	33.0	3.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	96.0	122.0	112.0	109.0	96.0	137.0	75.0	67.0	94.0	82.0	54.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

Snowflake connector¶

Data Preparation¶

Using all songs crashed my kernel¶

DBscan Modeling - YouTube¶

Grid search for optimum number of clusters¶

Determine optimum EPS¶

Determine optimum min_samples¶

Build model with optimum parameters¶

Remove the 0 songs¶

Optimize paramaters again for new model¶

Determine optimum clusters¶

Determine optimum eps¶

Build model with optimum parameters¶

Cluster -1 Country¶

Cluster -1 Genre¶

Cluster 0 Country¶

Cluster 0 Genre¶

Cluster 1 Country¶

Cluster 1 Genre¶

Cluster 2 Country¶

Cluster 2 Genre¶

Cluster 3 Country¶

Cluster 3 Genre¶

DBscan Modeling - Spotify¶

Grid search for optimum clusters (not running since we know clusters around 4-6 is optimal and great of biz insights)¶

Determine optimum EPS¶

Determine min_samples¶

Build model with optimum parameters¶

Remove cluster 0¶

Optimize parameters again for new YouTube model¶

Build new model with optimum parameters¶

spotify cluster -1 Country¶

spotify cluster -1 Genre¶

Spotify cluster 0 Country¶

Spotify cluster 0 Genre¶

Spotify cluster 1 Country¶

Spotify cluster 1 Genre¶

Spotify cluster 2 Country¶

Spotify cluster 2 Genre¶

Spotify Cluster 3 Country¶

Spotify Cluster 3 Genre¶